DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects
Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of mu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Yan [verfasserIn] Zhang, Haifeng [verfasserIn] Huang, Qingqing [verfasserIn] Han, Yan [verfasserIn] Zhao, Minghang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 241 |
|---|---|
| Übergeordnetes Werk: |
volume:241 |
| DOI / URN: |
10.1016/j.eswa.2023.122669 |
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| Katalog-ID: |
ELV066974968 |
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| 245 | 1 | 0 | |a DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects |
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| 520 | |a Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry. | ||
| 650 | 4 | |a Industrial defect | |
| 650 | 4 | |a YOLOv8 | |
| 650 | 4 | |a Small object detection | |
| 650 | 4 | |a Feature enhancement | |
| 650 | 4 | |a Feature fusion | |
| 650 | 4 | |a Anchor-free network | |
| 700 | 1 | |a Zhang, Haifeng |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Qingqing |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Minghang |e verfasserin |4 aut | |
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10.1016/j.eswa.2023.122669 doi (DE-627)ELV066974968 (ELSEVIER)S0957-4174(23)03171-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhang, Yan verfasserin (orcid)0000-0002-6242-4162 aut DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry. Industrial defect YOLOv8 Small object detection Feature enhancement Feature fusion Anchor-free network Zhang, Haifeng verfasserin aut Huang, Qingqing verfasserin aut Han, Yan verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241 |
| spelling |
10.1016/j.eswa.2023.122669 doi (DE-627)ELV066974968 (ELSEVIER)S0957-4174(23)03171-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhang, Yan verfasserin (orcid)0000-0002-6242-4162 aut DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry. Industrial defect YOLOv8 Small object detection Feature enhancement Feature fusion Anchor-free network Zhang, Haifeng verfasserin aut Huang, Qingqing verfasserin aut Han, Yan verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241 |
| allfields_unstemmed |
10.1016/j.eswa.2023.122669 doi (DE-627)ELV066974968 (ELSEVIER)S0957-4174(23)03171-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhang, Yan verfasserin (orcid)0000-0002-6242-4162 aut DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry. Industrial defect YOLOv8 Small object detection Feature enhancement Feature fusion Anchor-free network Zhang, Haifeng verfasserin aut Huang, Qingqing verfasserin aut Han, Yan verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241 |
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10.1016/j.eswa.2023.122669 doi (DE-627)ELV066974968 (ELSEVIER)S0957-4174(23)03171-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhang, Yan verfasserin (orcid)0000-0002-6242-4162 aut DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry. Industrial defect YOLOv8 Small object detection Feature enhancement Feature fusion Anchor-free network Zhang, Haifeng verfasserin aut Huang, Qingqing verfasserin aut Han, Yan verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241 |
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10.1016/j.eswa.2023.122669 doi (DE-627)ELV066974968 (ELSEVIER)S0957-4174(23)03171-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhang, Yan verfasserin (orcid)0000-0002-6242-4162 aut DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry. Industrial defect YOLOv8 Small object detection Feature enhancement Feature fusion Anchor-free network Zhang, Haifeng verfasserin aut Huang, Qingqing verfasserin aut Han, Yan verfasserin aut Zhao, Minghang verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241 |
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Zhang, Yan @@aut@@ Zhang, Haifeng @@aut@@ Huang, Qingqing @@aut@@ Han, Yan @@aut@@ Zhao, Minghang @@aut@@ |
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Zhang, Yan |
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Zhang, Yan ddc 004 bkl 54.72 misc Industrial defect misc YOLOv8 misc Small object detection misc Feature enhancement misc Feature fusion misc Anchor-free network DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects |
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004 VZ 54.72 bkl DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects Industrial defect YOLOv8 Small object detection Feature enhancement Feature fusion Anchor-free network |
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ddc 004 bkl 54.72 misc Industrial defect misc YOLOv8 misc Small object detection misc Feature enhancement misc Feature fusion misc Anchor-free network |
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ddc 004 bkl 54.72 misc Industrial defect misc YOLOv8 misc Small object detection misc Feature enhancement misc Feature fusion misc Anchor-free network |
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DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects |
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Zhang, Yan Zhang, Haifeng Huang, Qingqing Han, Yan Zhao, Minghang |
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dsp-yolo: an anchor-free network with dspan for small object detection of multiscale defects |
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DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects |
| abstract |
Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry. |
| abstractGer |
Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry. |
| abstract_unstemmed |
Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry. |
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DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects |
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The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. 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