Surface defect detection of threaded steel based on improved YOLOv5

doi:10.11996/JG.j.2095-302X.2023030427

Abstract

Abstract:

An improved YOLOv5 algorithm for surface defect detection was proposed to solve the problems of low detection accuracy, high missed detection, and false detection rate in industrial scenarios. The improved YOLOv5 algorithm incorporated the multi-space pyramid pooling module (M-SPP) to optimize the network and the detection accuracy could be improved to a certain extent by increasing the depth of the network for better feature extraction. The improved spatial and coordinate attention module (SCA) was introduced to further distinguish the weight relationship between different pixels in the spatial domain, put more emphasis on the region of interest. This algorithm reduced the unnecessary regional weight and enhanced the model’s attention to small target defects. The double sampling transition module (TB) was utilized for downsampling to reduce the loss of important features and obtain more feature information. The k-means ++ algorithm was also employed to reunite the class anchor frame, and the generated preset anchor frame was more suitable for different sizes of defects, thereby improving the detection accuracy of the algorithm. The experimental results on the surface defect dataset of spiral steel showed that the improved YOLOv5 algorithm achieved good detection performance for the surface defect detection of spiral steel, superior to other compared algorithms. The improved YOLOv5 algorithm achieved an AP₅₀ of 97.6%, 3.2% higher than the YOLOv5 algorithm, and all other indexes showed an increase. While maintaining the original detection speed, the algorithm could accurately detect the surface defects of steel rebar.

Key words: YOLOv5, defect detection, multi-spatial pyramid pooling, attention mechanism, double sampling transition, k-means++

CLC Number:

TP391

HU Xin, ZHOU Yun-qiang, XIAO Jian, YANG Jie. Surface defect detection of threaded steel based on improved YOLOv5[J]. Journal of Graphics, 2023, 44(3): 427-437.

Figures/Tables 15

References 29

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名称	实验配置
操作系统	ubantu20.04
编程语言	Python 3.8
深度学习框架	PyTorch 1.8.0
CPU	Intel Core i7-9700K
GPU	NVIDIA RTX 3050 (6 G)
Cuda	Cuda 11.2
平台	Pycharm 2022.1

名称	实验配置
操作系统	ubantu20.04
编程语言	Python 3.8
深度学习框架	PyTorch 1.8.0
CPU	Intel Core i7-9700K
GPU	NVIDIA RTX 3050 (6 G)
Cuda	Cuda 11.2
平台	Pycharm 2022.1

k-means++	M-SPP	SCA	TB	AP (%)	AP₅₀ (%)	AP₇₅ (%)	AP_S (%)	AP_M (%)	AP_L (%)
-	-	-	-	64.7	94.4	68.6	39.7	62.9	68.6
√	-	-	-	65.8	95.7	69.9	39.9	63.3	68.7
-	√	-	-	64.9	94.6	68.5	40.1	63.7	69.9
-	-	√	-	66.2	96.2	69.4	42.5	63.8	69.4
-	-	-	√	65.6	94.7	68.5	39.7	63.3	68.6
√	√	√	√	67.4	97.6	70.8	42.8	65.3	70.0

k-means++	M-SPP	SCA	TB	AP (%)	AP₅₀ (%)	AP₇₅ (%)	AP_S (%)	AP_M (%)	AP_L (%)
-	-	-	-	64.7	94.4	68.6	39.7	62.9	68.6
√	-	-	-	65.8	95.7	69.9	39.9	63.3	68.7
-	√	-	-	64.9	94.6	68.5	40.1	63.7	69.9
-	-	√	-	66.2	96.2	69.4	42.5	63.8	69.4
-	-	-	√	65.6	94.7	68.5	39.7	63.3	68.6
√	√	√	√	67.4	97.6	70.8	42.8	65.3	70.0

YOLOv5	Parameters (M)	AP_S (%)	AP_M (%)	AP_L (%)
+CA (GAP)	180.5	41.5	62.9	69.4
+CA (GMP)	180.5	41.9	63.1	69.3
+SCA (Ours)	180.5	42.5	63.8	69.4