Insulator Defect Detection Algorithm Based on Improved YOLOv11n
Abstract
:1. Introduction
- The C3k2 module within the backbone network was redesigned based on ODConv, leading to the proposition of the C3k2_ODConv module, in which the two ordinary convolutions in the original bottleneck structure are replaced by multidimensional dynamic convolutions, thereby effectively enhancing the feature extraction capability for irregular defects.
- Slimneck replaces the neck component of YOLOv11n, reducing both the model’s parameter count and computational complexity.
- The WIoU loss function is introduced to optimize the anchor frames to more accurately locate defect positions and speed up network convergence.
2. Methods
2.1. YOLOv11 Algorithm
2.2. Proposed Method
2.2.1. Improved C3k2 Module
2.2.2. Slimneck
2.2.3. WIoU Loss Function
3. Experiments and Results
3.1. Experimental Implementation
3.1.1. Experiment Platform
3.1.2. Dataset
3.1.3. Evaluation Indicators
- mAP50: The mean average precision at an intersection over union (IoU) threshold of 0.50.
- mAP50-95: The mean average precision calculated at IoU thresholds ranging from 0.50 to 0.95 (in increments of 0.05).
3.2. Comparative Experiment
3.3. Ablation Experiment
3.4. Visualization Experiment
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Setup |
---|---|
Batch size | 64 |
Image size | 640 × 640 |
Initial learning rate | 0.01 |
Final learning rate | 0.01 |
Weight decay | 0.0005 |
Momentum | 0.937 |
Optimizer | SGD |
Model | P | R | mAP50 | mAP50-95 | GFLOPs |
---|---|---|---|---|---|
Faster-RCNN | 0.856 | 0.783 | 0.817 | 0.477 | 50.6 |
SSD | 0.83 | 0.774 | 0.798 | 0.514 | 24.2 |
YOLOv3 | 0.851 | 0.8 | 0.824 | 0.536 | 14.3 |
YOLOv5 | 0.885 | 0.802 | 0.836 | 0.537 | 13.8 |
YOLOv8 | 0.909 | 0.785 | 0.854 | 0.559 | 28.4 |
YOLOv10 | 0.85 | 0.806 | 0.838 | 0.541 | 14.3 |
YOLOv11 | 0.906 | 0.84 | 0.873 | 0.574 | 7.6 |
Ours | 0.918 | 0.881 | 0.91 | 0.619 | 6.5 |
Model | P | R | mAP50 | mAP50-95 |
---|---|---|---|---|
Y-CIoU | 0.906 | 0.84 | 0.873 | 0.574 |
Y-EIoU | 0.868 | 0.841 | 0.872 | 0.579 |
Y-DIoU | 0.909 | 0.838 | 0.875 | 0.581 |
Y-SIoU | 0.907 | 0.834 | 0.872 | 0.578 |
Y-GIoU | 0.897 | 0.839 | 0.876 | 0.582 |
Y-WIoU | 0.899 | 0.845 | 0.888 | 0.588 |
Model | C3k2_ODConv | Slimneck | WIoU | P | R | mAP50 | mAP50-95 | FPS | Params/M |
---|---|---|---|---|---|---|---|---|---|
Model 1 | 0.906 | 0.84 | 0.873 | 0.574 | 186 | 2.58 | |||
Model 2 | Y | 0.934 | 0.829 | 0.88 | 0.577 | 172 | 2.65 | ||
Model 3 | Y | 0.91 | 0.848 | 0.876 | 0.579 | 234 | 2.10 | ||
Model 4 | Y | 0.899 | 0.845 | 0.888 | 0.588 | 208 | 2.58 | ||
Model 5 | Y | Y | 0.908 | 0.858 | 0.889 | 0.594 | 214 | 2.32 | |
Model 6 | Y | Y | 0.935 | 0.844 | 0.89 | 0.599 | 195 | 2.64 | |
Model 7 | Y | Y | 0.912 | 0.843 | 0.893 | 0.593 | 241 | 2.15 | |
ours | Y | Y | Y | 0.918 | 0.881 | 0.91 | 0.619 | 228 | 2.18 |
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Zhao, J.; Miao, S.; Kang, R.; Cao, L.; Zhang, L.; Ren, Y. Insulator Defect Detection Algorithm Based on Improved YOLOv11n. Sensors 2025, 25, 1327. https://doi.org/10.3390/s25051327
Zhao J, Miao S, Kang R, Cao L, Zhang L, Ren Y. Insulator Defect Detection Algorithm Based on Improved YOLOv11n. Sensors. 2025; 25(5):1327. https://doi.org/10.3390/s25051327
Chicago/Turabian StyleZhao, Junmei, Shangxiao Miao, Rui Kang, Longkun Cao, Liping Zhang, and Yifeng Ren. 2025. "Insulator Defect Detection Algorithm Based on Improved YOLOv11n" Sensors 25, no. 5: 1327. https://doi.org/10.3390/s25051327
APA StyleZhao, J., Miao, S., Kang, R., Cao, L., Zhang, L., & Ren, Y. (2025). Insulator Defect Detection Algorithm Based on Improved YOLOv11n. Sensors, 25(5), 1327. https://doi.org/10.3390/s25051327