Research on YOLOv7-based defect detection method for automotive running lights

Figures & data

Figure 1. Original YOLOv7 network structure.

Figure 2. Original ELAN module structure.

Figure 3. Recursive Gated Convolution structure.

Figure 4. Global attention mechanism structure.

Figure 5. GhostConv.

Figure 6. Comparisons of different dimensions of attention.

Figure 7. Improved YOLOv7 network structure.

Figure 8. Improved ELAN module.

Table 1. Experimental environment.

Python IDE	Pycharm2020
Python Interpreter	Python 3.6
Machine learning framework	Pytorch 1.10.2
Parallel computing platform	CUDA 11.2
CPU	Intel i7-10700
GPU	NVIDIA GeForce RTX -3060

Figure 9. Headlights and camera equipment.

Figure 10. Example of running light defect.

Table 2. Contrast experiment.

Model	Weight/(M)	Precision	Recall	mAP@0.5	mAP@.5:.95	FPS
YOLOv3	118	0.887	0.675	73.5%	50.9%	41.32
YOLOv4	244	0.8551	0.7111	80.2%	42.1%	42.96
YOLOv5	88.7	0.831	0.768	80.8%	56.8%	47.62
YOLOv7	71.5	0.86	0.793	85.7%	57.7%	72.46
ours	72.8	0.839	0.864	89.7%	62.9%	61.35

Table 3. Ablation experiments.

Model	Weight/(M)	mAP@0.5	mAP@.5:.95	FPS
YOLOv7	71.5	85.7%	57.7%	72.46
YOLOv7-g^nconv	77.3	85.9%	59.2%	68.49
YOLOv7-SimAM	68.7	85%	58.2%	76.34
YOLOv7-GhostConv+GAM	69.8	87.3%	61.2%	64.94
ours	72.8	89.7%	62.9%	61.35

Table 4. Versatility experiments with metal casting dataset.

Model	Weight/(M)	mAP@0.5	FPS
YOLOv3	117	55%	37.59
YOLOv4	244	69.91%	42.39
YOLOv5	237	63.1%	23.04
YOLOv7	71.3	75.9%	60.98
ours	72.6	77.2%	56.82

Table 5. Relationship between input-sizes and output-sizes.

input-sizes	512$\times$512	640$\times$640	800$\times$800
output-sizes	16$\times$16	20$\times$20	25$\times$25
	32$\times$32	40$\times$40	50$\times$50
	64$\times$64	80$\times$80	100$\times$100

Table 6. Performance of different image input-sizes.

input-size	mAP@0.5	mAP@.5:.95	FPS
512$\times$512	85.8%	58.2%	63.29
640$\times$640	89.7%	62.9%	61.35
800$\times$800	87.8%	60.5%	43.86

Figure 11. Identification results of different types of defects.

Figure 12. Tracking of the first light beads.