SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms
The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCF...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, Shenshen [verfasserIn] Chen, Haiyong [verfasserIn] Wang, Chuhan [verfasserIn] Shi, Shijie [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Measurement - Amsterdam [u.a.] : Elsevier Science, 1983, 206 |
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| Übergeordnetes Werk: |
volume:206 |
| DOI / URN: |
10.1016/j.measurement.2022.112342 |
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| Katalog-ID: |
ELV009033777 |
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| 245 | 1 | 0 | |a SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms |
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| 520 | |a The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods. | ||
| 650 | 4 | |a Photovoltaic farms | |
| 650 | 4 | |a Small-scale defect | |
| 650 | 4 | |a Complex background | |
| 650 | 4 | |a Scale-aware | |
| 700 | 1 | |a Chen, Haiyong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chuhan |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Shijie |e verfasserin |4 aut | |
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10.1016/j.measurement.2022.112342 doi (DE-627)ELV009033777 (ELSEVIER)S0263-2241(22)01538-X DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Zhao, Shenshen verfasserin aut SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods. Photovoltaic farms Small-scale defect Complex background Scale-aware Chen, Haiyong verfasserin aut Wang, Chuhan verfasserin aut Shi, Shijie verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 206 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:206 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 206 |
| spelling |
10.1016/j.measurement.2022.112342 doi (DE-627)ELV009033777 (ELSEVIER)S0263-2241(22)01538-X DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Zhao, Shenshen verfasserin aut SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods. Photovoltaic farms Small-scale defect Complex background Scale-aware Chen, Haiyong verfasserin aut Wang, Chuhan verfasserin aut Shi, Shijie verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 206 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:206 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 206 |
| allfields_unstemmed |
10.1016/j.measurement.2022.112342 doi (DE-627)ELV009033777 (ELSEVIER)S0263-2241(22)01538-X DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Zhao, Shenshen verfasserin aut SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods. Photovoltaic farms Small-scale defect Complex background Scale-aware Chen, Haiyong verfasserin aut Wang, Chuhan verfasserin aut Shi, Shijie verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 206 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:206 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 206 |
| allfieldsGer |
10.1016/j.measurement.2022.112342 doi (DE-627)ELV009033777 (ELSEVIER)S0263-2241(22)01538-X DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Zhao, Shenshen verfasserin aut SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods. Photovoltaic farms Small-scale defect Complex background Scale-aware Chen, Haiyong verfasserin aut Wang, Chuhan verfasserin aut Shi, Shijie verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 206 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:206 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 206 |
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10.1016/j.measurement.2022.112342 doi (DE-627)ELV009033777 (ELSEVIER)S0263-2241(22)01538-X DE-627 ger DE-627 rda eng 660 VZ 50.21 bkl Zhao, Shenshen verfasserin aut SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods. Photovoltaic farms Small-scale defect Complex background Scale-aware Chen, Haiyong verfasserin aut Wang, Chuhan verfasserin aut Shi, Shijie verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 206 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:206 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_63 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik VZ AR 206 |
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Zhao, Shenshen Chen, Haiyong Wang, Chuhan Shi, Shijie |
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sncf-net: scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms |
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SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms |
| abstract |
The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods. |
| abstractGer |
The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods. |
| abstract_unstemmed |
The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods. |
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| title_short |
SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms |
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Chen, Haiyong Wang, Chuhan Shi, Shijie |
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