Improved insulator location and defect detection method based on GhostNet and YOLOv5s networks
Outdoor, real-time, and accurate detection of insulator defect locations can effectively avoid the occurrence of power grid security accidents. This paper proposes an improved GhostNet-YOLOv5s algorithm based on GhostNet and YOLOv5 models. First, the backbone feature extraction network of YOLOv5 was...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-09-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024242 |
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| _version_ | 1832590817474117632 |
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| author | Jianjun Huang Xuhong Huang Ronghao Kang Zhihong Chen Junhan Peng |
| author_facet | Jianjun Huang Xuhong Huang Ronghao Kang Zhihong Chen Junhan Peng |
| author_sort | Jianjun Huang |
| collection | DOAJ |
| description | Outdoor, real-time, and accurate detection of insulator defect locations can effectively avoid the occurrence of power grid security accidents. This paper proposes an improved GhostNet-YOLOv5s algorithm based on GhostNet and YOLOv5 models. First, the backbone feature extraction network of YOLOv5 was reconstructed with the lightweight GhostNet module to reduce the number of parameters and floating point operations of the model, so as to achieve the purpose of being lightweight. Then, a 160 × 160 feature layer was added to the YOLOv5 network to extract more feature information of small targets and fuzzy targets. In addition, the introduction of lightweight GSConv convolution in the neck network further reduced the computing cost of the entire network. Finally, Focal-EIoU was introduced to optimize the CIoU bounding box regression loss function in the original algorithm to improve the convergence speed and target location accuracy of the model. The experimental results show that the parameter number, computation amount, and model size of the GhostNet-YOLOv5s model are reduced by 40%, 25%, and 36%, respectively, compared with the unimproved YOLOv5s model. The proposed method not only ensures the precision of insulator defect detection, but also greatly decreases the complexity of the model. Therefore, the GhostNet-YOLOv5s algorithm can meet the requirements of real-time detection in complex outdoor environments. |
| format | Article |
| id | doaj-art-68a64dc47d8c4147b1245a000deb395c |
| institution | Kabale University |
| issn | 2688-1594 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-68a64dc47d8c4147b1245a000deb395c2025-01-23T07:52:41ZengAIMS PressElectronic Research Archive2688-15942024-09-013295249526710.3934/era.2024242Improved insulator location and defect detection method based on GhostNet and YOLOv5s networksJianjun Huang0Xuhong Huang1Ronghao Kang2Zhihong Chen3Junhan Peng4School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaOutdoor, real-time, and accurate detection of insulator defect locations can effectively avoid the occurrence of power grid security accidents. This paper proposes an improved GhostNet-YOLOv5s algorithm based on GhostNet and YOLOv5 models. First, the backbone feature extraction network of YOLOv5 was reconstructed with the lightweight GhostNet module to reduce the number of parameters and floating point operations of the model, so as to achieve the purpose of being lightweight. Then, a 160 × 160 feature layer was added to the YOLOv5 network to extract more feature information of small targets and fuzzy targets. In addition, the introduction of lightweight GSConv convolution in the neck network further reduced the computing cost of the entire network. Finally, Focal-EIoU was introduced to optimize the CIoU bounding box regression loss function in the original algorithm to improve the convergence speed and target location accuracy of the model. The experimental results show that the parameter number, computation amount, and model size of the GhostNet-YOLOv5s model are reduced by 40%, 25%, and 36%, respectively, compared with the unimproved YOLOv5s model. The proposed method not only ensures the precision of insulator defect detection, but also greatly decreases the complexity of the model. Therefore, the GhostNet-YOLOv5s algorithm can meet the requirements of real-time detection in complex outdoor environments.https://www.aimspress.com/article/doi/10.3934/era.2024242insulatoryolov5defect detectionlightweightghostnet |
| spellingShingle | Jianjun Huang Xuhong Huang Ronghao Kang Zhihong Chen Junhan Peng Improved insulator location and defect detection method based on GhostNet and YOLOv5s networks Electronic Research Archive insulator yolov5 defect detection lightweight ghostnet |
| title | Improved insulator location and defect detection method based on GhostNet and YOLOv5s networks |
| title_full | Improved insulator location and defect detection method based on GhostNet and YOLOv5s networks |
| title_fullStr | Improved insulator location and defect detection method based on GhostNet and YOLOv5s networks |
| title_full_unstemmed | Improved insulator location and defect detection method based on GhostNet and YOLOv5s networks |
| title_short | Improved insulator location and defect detection method based on GhostNet and YOLOv5s networks |
| title_sort | improved insulator location and defect detection method based on ghostnet and yolov5s networks |
| topic | insulator yolov5 defect detection lightweight ghostnet |
| url | https://www.aimspress.com/article/doi/10.3934/era.2024242 |
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