Substation Equipment Defect Detection Based on Improved YOLOv8
The detection of equipment defects in substations is crucial for maintaining the normal operation of power systems. This paper proposes an object detection algorithm for substation equipment defect detection based on improvements to the YOLOv8 model. First, the backbone of YOLOv8 is replaced with Ef...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/11/3410 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850158937192529920 |
|---|---|
| author | Yiwei Sun Xiangran Sun Ying Lin Yi Yang Zhuangzhuang Li Lun Du Chaojun Shi |
| author_facet | Yiwei Sun Xiangran Sun Ying Lin Yi Yang Zhuangzhuang Li Lun Du Chaojun Shi |
| author_sort | Yiwei Sun |
| collection | DOAJ |
| description | The detection of equipment defects in substations is crucial for maintaining the normal operation of power systems. This paper proposes an object detection algorithm for substation equipment defect detection based on improvements to the YOLOv8 model. First, the backbone of YOLOv8 is replaced with EfficientViT, which not only reduces computational redundancy but also enhances the model’s feature extraction capabilities, thereby improving overall performance. Second, a Squeeze-and-Excitation (SE) attention mechanism module is incorporated at the terminal stage of the backbone network to reinforce channel-wise feature representation in input feature maps. Finally, the Bottleneck component within YOLOv8’s C2f module is substituted with FasterBlock, which significantly accelerates inference speed while maintaining model accuracy. Experimental results on the substation equipment defect dataset demonstrate that the improved algorithm achieves a mean average precision (mAP) of 92.8%, representing a 1.8% enhancement over the baseline model. The substantial improvement in average precision confirms the feasibility and effectiveness of the proposed modifications to the YOLOv8 architecture. |
| format | Article |
| id | doaj-art-8fd33587f98e428d9a4e383cb933a234 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8fd33587f98e428d9a4e383cb933a2342025-08-20T02:23:44ZengMDPI AGSensors1424-82202025-05-012511341010.3390/s25113410Substation Equipment Defect Detection Based on Improved YOLOv8Yiwei Sun0Xiangran Sun1Ying Lin2Yi Yang3Zhuangzhuang Li4Lun Du5Chaojun Shi6State Grid Shandong Electric Power Research Institute, Jinan 250003, ChinaState Grid Tancheng Power Supply Company, Linyi 276100, ChinaState Grid Shandong Electric Power Research Institute, Jinan 250003, ChinaState Grid Shandong Electric Power Research Institute, Jinan 250003, ChinaState Grid Shandong Electric Power Research Institute, Jinan 250003, ChinaState Grid Shandong Electric Power Research Institute, Jinan 250003, ChinaDepartment of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, ChinaThe detection of equipment defects in substations is crucial for maintaining the normal operation of power systems. This paper proposes an object detection algorithm for substation equipment defect detection based on improvements to the YOLOv8 model. First, the backbone of YOLOv8 is replaced with EfficientViT, which not only reduces computational redundancy but also enhances the model’s feature extraction capabilities, thereby improving overall performance. Second, a Squeeze-and-Excitation (SE) attention mechanism module is incorporated at the terminal stage of the backbone network to reinforce channel-wise feature representation in input feature maps. Finally, the Bottleneck component within YOLOv8’s C2f module is substituted with FasterBlock, which significantly accelerates inference speed while maintaining model accuracy. Experimental results on the substation equipment defect dataset demonstrate that the improved algorithm achieves a mean average precision (mAP) of 92.8%, representing a 1.8% enhancement over the baseline model. The substantial improvement in average precision confirms the feasibility and effectiveness of the proposed modifications to the YOLOv8 architecture.https://www.mdpi.com/1424-8220/25/11/3410object detectionsubstation equipmentdefect detectionYOLOv8 |
| spellingShingle | Yiwei Sun Xiangran Sun Ying Lin Yi Yang Zhuangzhuang Li Lun Du Chaojun Shi Substation Equipment Defect Detection Based on Improved YOLOv8 Sensors object detection substation equipment defect detection YOLOv8 |
| title | Substation Equipment Defect Detection Based on Improved YOLOv8 |
| title_full | Substation Equipment Defect Detection Based on Improved YOLOv8 |
| title_fullStr | Substation Equipment Defect Detection Based on Improved YOLOv8 |
| title_full_unstemmed | Substation Equipment Defect Detection Based on Improved YOLOv8 |
| title_short | Substation Equipment Defect Detection Based on Improved YOLOv8 |
| title_sort | substation equipment defect detection based on improved yolov8 |
| topic | object detection substation equipment defect detection YOLOv8 |
| url | https://www.mdpi.com/1424-8220/25/11/3410 |
| work_keys_str_mv | AT yiweisun substationequipmentdefectdetectionbasedonimprovedyolov8 AT xiangransun substationequipmentdefectdetectionbasedonimprovedyolov8 AT yinglin substationequipmentdefectdetectionbasedonimprovedyolov8 AT yiyang substationequipmentdefectdetectionbasedonimprovedyolov8 AT zhuangzhuangli substationequipmentdefectdetectionbasedonimprovedyolov8 AT lundu substationequipmentdefectdetectionbasedonimprovedyolov8 AT chaojunshi substationequipmentdefectdetectionbasedonimprovedyolov8 |