Defect detection method of rubber seal ring based on improved YOLOv7-tiny

doi:10.11996/JG.j.2095-302X.2024030446

Abstract

Abstract:

Aiming at the problem of low efficiency in traditional detection of surface defects of rubber seal rings, an improved YOLOv7-tiny algorithm for surface defect detection of rubber seal rings was proposed. The PConv optimized ELAN structure was introduced into the backbone feature extraction network to enhance the algorithm’s feature extraction capability and to reduce the number of parameters. The global attention mechanism (GAM) was introduced into the feature fusion network, utilizing the attention weights between each pair of 3D channels, spatial widths, and spatial heights to improve efficiency by capturing the important features in three dimensions, thus enhancing the algorithm’s feature fusion capability. The WIoU loss function was employed to optimize the original bounding box loss function, enhancing the algorithm’s ability to locate the detected targets through a situation-compliant gradient gain allocation strategy. Additionally, a P2 small-target detection layer was added to strengthen the fusion of the deep and shallow feature information, thereby enhancing the algorithm’s ability to detect small-target defects. Experimental comparisons were conducted using the O-Rings dataset. The improved algorithm was compared with the YOLOv7-tiny algorithm, resulting in a 7.8% improvement in mAP and achieving a detection accuracy of 90.9%, meeting the needs of actual industrial production.

Key words: YOLOv7-tiny, rubber seal ring, defect detection, attention mechanism, small target detection layer

CLC Number:

TP391

ZHANG Xiangsheng, YANG Xiao. Defect detection method of rubber seal ring based on improved YOLOv7-tiny[J]. Journal of Graphics, 2024, 45(3): 446-453.

Figures/Tables 12

Fig. 1 Network structure of the YOLOv7-tiny algorithm

Fig. 2 PConv and conventional convolutional structures ((a) Regular convolution; (b) Partial convolution (PConv))

Fig. 3 ELAN-P structure

Fig. 4 GAM structure

Fig. 5 Channel attention submodule

Fig. 6 Space attention submodule

Fig. 7 Improved YOLOv7-tiny network structure

Fig. 8 Sample plot of rubber sealing ring defect data set ((a) Coarse; (b) Nick; (c) Crack; (d) Damage)

Table 1 Rubber seal defect data set

缺陷类型	标签	数量/张
划痕	Nick	3 620
断裂	Crack	3 490
缺料	Damage	4 250
表面粗糙	Coarse	3 080

Table 2 Comparison of ablation experiment results

组别	Model	P/%	R/%	F1-score	mAP@0.5/%	Params/M	Time/ms
1组	YOLOv7-tiny	77.6	82.8	80.1	83.1	6.02	16.7
2组	YOLOv7-tiny+A	78.8	83.8	81.2	84.5	4.74	15.5
3组	YOLOv7-tiny+A+B	84.3	85.4	84.8	88.8	6.38	17.1
4组	YOLOv7-tiny+A+B+C	86.4	90.1	88.2	89.5	6.38	17.3
5组	YOLOv7-tiny+A+B+C+D	93.1	88.3	90.6	90.9	6.79	18.5

Table 3 Comparison of results of contrasting experiments

Model	mAP@0.5/%	Params/M	Time/ms
SSD	67.1	24.48	49.9
Faster RCNN	81.5	72.02	38.1
YOLOv5s	72.2	7.20	29.9
文献[11]	82.2	23.90	23.3
YOLOX-tiny	79.2	5.19	18.5
YOLOv6s	78.8	17.20	25.0
YOLOv7	88.9	37.20	40.4
文献[19]	89.1	42.80	40.5
YOLOv7-tiny	82.9	6.02	16.7
YOLOv8s	84.8	11.10	16.2
本文算法	90.9	6.79	18.5

Fig. 9 Comparison of the effect of detecting defects in rubber seals ((a) Nick; (b) Damage; (c) Coarse; (d) Crack)

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