Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss
Abstract
:1. Introduction
2. Materials and Methods
2.1. YOLOv5
2.2. EL Images
2.3. Cascaded Detection Network
2.4. Focal-EIoU Loss
- Center Point Distance Loss: This component aims to minimize the distance between the center points of the predicted bounding box and the ground truth bounding box. By doing so, it improves the accuracy of localizing the object within the bounding box;
- Consideration of Size Differences: EIoU penalizes discrepancies in width and height between the predicted bounding box and the ground truth bounding box. This penalty mechanism ensures that the predicted bounding box closely aligns with the shape and size of the actual object, thereby refining the detection accuracy;
- Incorporation of Minimum Enclosing Box Size: By integrating the size of the minimum enclosing box that encompasses both the predicted and ground truth bounding boxes into the loss function, EIoU becomes more attuned to the object’s size and position. This inclusion enhances the sensitivity of the loss function to variations in object size and spatial orientation, contributing to more precise object localization.
3. Results
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Environment | Configuration |
---|---|
CPU | Intel(R) Core(TM) i7-10700 CPU @ 2.90 GHz 2.90 GHz |
GPU | NVIDIA GeForce GTX 3060 |
System | Windows 10 |
Language | Python 3.8 |
Architecture | Pytorch |
Loss | P | R | F1 | mAP |
---|---|---|---|---|
DIoU | 0.724 | 0.826 | 0.858 | 0.819 |
CIoU | 0.758 | 0.844 | 0.857 | 0.847 |
Focal-EIoU | 0.792 | 0.850 | 0.933 | 0.865 |
Defects | DIoU | CIoU | Focal-EIoU |
---|---|---|---|
(a) SE offset | 0.714 | 0.721 | 0.837 |
(b) Dark spots | 0.728 | 0.758 | 0.850 |
(c) Black spots | 0.745 | 0.824 | 0.830 |
(d) Broken girds | 0.822 | 0.765 | 0.866 |
(e) False marks | 0.798 | 0.847 | 0.843 |
(f) Linear recessive cracks | 0.743 | 0.775 | 0.827 |
(g) Branch recessive cracks | 0.703 | 0.752 | 0.858 |
(h) Fragment | 0.850 | 0.853 | 0.876 |
(i) Flurry filaments | 0.761 | 0.810 | 0.855 |
(j) Cleaning traces | 0.755 | 0.768 | 0.823 |
(k) Current unevenness | 0.812 | 0.841 | 0.906 |
(l) Black cell | 0.884 | 0.905 | 0.958 |
Algorithms | mAP | Speed |
---|---|---|
SVM [22] | 0.648 | 500 ms |
Faster RCNN [24] | 0.837 | 50 ms |
SSD | 0.750 | 90 ms |
YOLOv3 [36] | 0.671 | 70 ms |
YOLOv4 [37] | 0.734 | 30 ms |
YOLOv5s [40] | 0.793 | 20 ms |
YOLOv6s [46] | 0.782 | 60 ms |
YOLOv7s [47] | 0.811 | 80 ms |
Proposed | 0.865 | 20 ms |
Method | mAP | Speed |
---|---|---|
YOLOv5m | 0.791 | 23.8 ms |
YOLOv5m * | 0.857 | 23.8 ms |
YOLOv5l | 0.798 | 32.6 ms |
YOLOv5l * | 0.862 | 32.6 ms |
YOLOv5x | 0.802 | 46.8 ms |
YOLOv5x * | 0.867 | 46.8 ms |
YOLOv5s | 0.793 | 20.0 ms |
YOLOv5s * | 0.865 | 20.0 ms |
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Ding, S.; Jing, W.; Chen, H.; Chen, C. Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss. Appl. Sci. 2024, 14, 7493. https://doi.org/10.3390/app14177493
Ding S, Jing W, Chen H, Chen C. Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss. Applied Sciences. 2024; 14(17):7493. https://doi.org/10.3390/app14177493
Chicago/Turabian StyleDing, Shen, Wanting Jing, Hao Chen, and Congyan Chen. 2024. "Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss" Applied Sciences 14, no. 17: 7493. https://doi.org/10.3390/app14177493