An insulator target detection algorithm based on improved YOLOv5
Abstract Drone inspections are widely utilized in the detection of insulators in power lines. To address issues with traditional object detection algorithms, such as large parameter counts, low detection accuracy, and high miss rates, this paper proposes an insulator detection algorithm based on an...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84623-6 |
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| _version_ | 1841559733447688192 |
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| author | Bing Zeng Zhihao Zhou Yu Zhou Dilin He Zhanpeng Liao Zihan Jin Yulu Zhou Kexin Yi Yunmin Xie Wenhua Zhang |
| author_facet | Bing Zeng Zhihao Zhou Yu Zhou Dilin He Zhanpeng Liao Zihan Jin Yulu Zhou Kexin Yi Yunmin Xie Wenhua Zhang |
| author_sort | Bing Zeng |
| collection | DOAJ |
| description | Abstract Drone inspections are widely utilized in the detection of insulators in power lines. To address issues with traditional object detection algorithms, such as large parameter counts, low detection accuracy, and high miss rates, this paper proposes an insulator detection algorithm based on an improved YOLOv5 model. Firstly, in the backbone and neck networks, a lightweight CSP-SCConv module is employed to replace the original CSP-Darknet53 module, thereby reducing the parameter count and enhancing the feature extraction capabilities. Secondly, to broaden the image receptive field and improve feature fusion, a Receptive Field Block (RFB) model is incorporated into the neck network, replacing the original Spatial Pyramid Pooling Fast (SPPF) module. Additionally, a Lattice Structured Kernel (LSKBlock) attention mechanism is appended at the end of the neck network to further obtain richer semantic information. Finally, to flexibly improve the accuracy of bounding boxes of different sizes and enhance the robustness of the model, an $$\alpha CIOU$$ loss function is utilized to replace the original Complete Intersection Over Union (CIOU) loss function. Experimental results demonstrate that the improved YOLOv5 model achieves a mean Average Precision (mAP) precision of 95.60%, with a parameter count of 18.36 M and a computational load of 30.10G, respectively. The Precision (P) and Recall (R) are 88.10% and 95.20%, providing strong support for deployment on mobile devices for real-time detection. |
| format | Article |
| id | doaj-art-52923156073c4ce19e9911a07969e4cf |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-52923156073c4ce19e9911a07969e4cf2025-01-05T12:14:19ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-84623-6An insulator target detection algorithm based on improved YOLOv5Bing Zeng0Zhihao Zhou1Yu Zhou2Dilin He3Zhanpeng Liao4Zihan Jin5Yulu Zhou6Kexin Yi7Yunmin Xie8Wenhua Zhang9Nanchang Institute of TechnologyNanchang Institute of TechnologyNanchang Institute of TechnologyNanchang Institute of TechnologyNanchang Institute of TechnologyNanchang Institute of TechnologyNanchang Institute of TechnologyNanchang Institute of TechnologyNanchang Institute of TechnologyNanchang Institute of TechnologyAbstract Drone inspections are widely utilized in the detection of insulators in power lines. To address issues with traditional object detection algorithms, such as large parameter counts, low detection accuracy, and high miss rates, this paper proposes an insulator detection algorithm based on an improved YOLOv5 model. Firstly, in the backbone and neck networks, a lightweight CSP-SCConv module is employed to replace the original CSP-Darknet53 module, thereby reducing the parameter count and enhancing the feature extraction capabilities. Secondly, to broaden the image receptive field and improve feature fusion, a Receptive Field Block (RFB) model is incorporated into the neck network, replacing the original Spatial Pyramid Pooling Fast (SPPF) module. Additionally, a Lattice Structured Kernel (LSKBlock) attention mechanism is appended at the end of the neck network to further obtain richer semantic information. Finally, to flexibly improve the accuracy of bounding boxes of different sizes and enhance the robustness of the model, an $$\alpha CIOU$$ loss function is utilized to replace the original Complete Intersection Over Union (CIOU) loss function. Experimental results demonstrate that the improved YOLOv5 model achieves a mean Average Precision (mAP) precision of 95.60%, with a parameter count of 18.36 M and a computational load of 30.10G, respectively. The Precision (P) and Recall (R) are 88.10% and 95.20%, providing strong support for deployment on mobile devices for real-time detection.https://doi.org/10.1038/s41598-024-84623-6YOLOv5InsulatorCSP-SCConvRFBLSKBlock |
| spellingShingle | Bing Zeng Zhihao Zhou Yu Zhou Dilin He Zhanpeng Liao Zihan Jin Yulu Zhou Kexin Yi Yunmin Xie Wenhua Zhang An insulator target detection algorithm based on improved YOLOv5 Scientific Reports YOLOv5 Insulator CSP-SCConv RFB LSKBlock |
| title | An insulator target detection algorithm based on improved YOLOv5 |
| title_full | An insulator target detection algorithm based on improved YOLOv5 |
| title_fullStr | An insulator target detection algorithm based on improved YOLOv5 |
| title_full_unstemmed | An insulator target detection algorithm based on improved YOLOv5 |
| title_short | An insulator target detection algorithm based on improved YOLOv5 |
| title_sort | insulator target detection algorithm based on improved yolov5 |
| topic | YOLOv5 Insulator CSP-SCConv RFB LSKBlock |
| url | https://doi.org/10.1038/s41598-024-84623-6 |
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