Multi-class object detection system using hybrid convolutional neural network architecture
Abstract Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Borade, Jay Laxman [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2022), 22 vom: 11. Apr., Seite 31727-31751 |
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| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:22 ; day:11 ; month:04 ; pages:31727-31751 |
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| DOI / URN: |
10.1007/s11042-022-13007-7 |
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OLC2079391704 |
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| 520 | |a Abstract Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SVR (Support Vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL VOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL VOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with recently developed works such as YOLO-fine, EfficientDet, YOLOv4, RetinaNet, GCNet and HRNet architectures. | ||
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10.1007/s11042-022-13007-7 doi (DE-627)OLC2079391704 (DE-He213)s11042-022-13007-7-p DE-627 ger DE-627 rakwb eng 070 004 VZ Borade, Jay Laxman verfasserin aut Multi-class object detection system using hybrid convolutional neural network architecture 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SVR (Support Vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL VOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL VOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with recently developed works such as YOLO-fine, EfficientDet, YOLOv4, RetinaNet, GCNet and HRNet architectures. Image processing Object localization Deep learning Object recognition Machine learning Lakshmi, Muddana A aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 22 vom: 11. Apr., Seite 31727-31751 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:22 day:11 month:04 pages:31727-31751 https://doi.org/10.1007/s11042-022-13007-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 22 11 04 31727-31751 |
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10.1007/s11042-022-13007-7 doi (DE-627)OLC2079391704 (DE-He213)s11042-022-13007-7-p DE-627 ger DE-627 rakwb eng 070 004 VZ Borade, Jay Laxman verfasserin aut Multi-class object detection system using hybrid convolutional neural network architecture 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SVR (Support Vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL VOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL VOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with recently developed works such as YOLO-fine, EfficientDet, YOLOv4, RetinaNet, GCNet and HRNet architectures. Image processing Object localization Deep learning Object recognition Machine learning Lakshmi, Muddana A aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 22 vom: 11. Apr., Seite 31727-31751 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:22 day:11 month:04 pages:31727-31751 https://doi.org/10.1007/s11042-022-13007-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 22 11 04 31727-31751 |
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10.1007/s11042-022-13007-7 doi (DE-627)OLC2079391704 (DE-He213)s11042-022-13007-7-p DE-627 ger DE-627 rakwb eng 070 004 VZ Borade, Jay Laxman verfasserin aut Multi-class object detection system using hybrid convolutional neural network architecture 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SVR (Support Vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL VOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL VOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with recently developed works such as YOLO-fine, EfficientDet, YOLOv4, RetinaNet, GCNet and HRNet architectures. Image processing Object localization Deep learning Object recognition Machine learning Lakshmi, Muddana A aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 22 vom: 11. Apr., Seite 31727-31751 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:22 day:11 month:04 pages:31727-31751 https://doi.org/10.1007/s11042-022-13007-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 22 11 04 31727-31751 |
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multi-class object detection system using hybrid convolutional neural network architecture |
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Multi-class object detection system using hybrid convolutional neural network architecture |
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Abstract Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SVR (Support Vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL VOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL VOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with recently developed works such as YOLO-fine, EfficientDet, YOLOv4, RetinaNet, GCNet and HRNet architectures. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SVR (Support Vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL VOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL VOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with recently developed works such as YOLO-fine, EfficientDet, YOLOv4, RetinaNet, GCNet and HRNet architectures. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SVR (Support Vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL VOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL VOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with recently developed works such as YOLO-fine, EfficientDet, YOLOv4, RetinaNet, GCNet and HRNet architectures. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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