Steel surface defect detection algorithm based on improved YOLOv8n

Home > Archive>Volume 47, Issue 13, 2024 >191-198

Steel surface defect detection algorithm based on improved YOLOv8n
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                        Zhao YangZhao Yang
1.School of Computer Science and Technology, Shenyang University of Chemical Technology，Shenyang 110142, China； 2.Liaoning Provincial Key Laboratory of Chemical Process Industry and Intelligent Technology，Shenyang 110142, China
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Wang JunkaiWang Junkai
1.School of Computer Science and Technology, Shenyang University of Chemical Technology，Shenyang 110142, China； 2.Liaoning Provincial Key Laboratory of Chemical Process Industry and Intelligent Technology，Shenyang 110142, China
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Lin ZhiyiLin Zhiyi
1.School of Computer Science and Technology, Shenyang University of Chemical Technology，Shenyang 110142, China； 2.Liaoning Provincial Key Laboratory of Chemical Process Industry and Intelligent Technology，Shenyang 110142, China
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Zhou ZhongxiangZhou Zhongxiang
1.School of Computer Science and Technology, Shenyang University of Chemical Technology，Shenyang 110142, China； 2.Liaoning Provincial Key Laboratory of Chemical Process Industry and Intelligent Technology，Shenyang 110142, China
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Xu SenXu Sen
1.School of Computer Science and Technology, Shenyang University of Chemical Technology，Shenyang 110142, China； 2.Liaoning Provincial Key Laboratory of Chemical Process Industry and Intelligent Technology，Shenyang 110142, China
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Affiliation:1.School of Computer Science and Technology, Shenyang University of Chemical Technology，Shenyang 110142, China; 2.Liaoning Provincial Key Laboratory of Chemical Process Industry and Intelligent Technology，Shenyang 110142, China
Clc Number:TN911
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Abstract:

To address the challenges posed by the diverse types of defects, significant size variations, and high complexity of existing models with insufficient detection accuracy in steel surface defect detection, this paper proposes a detection algorithm named YOLOv8-ODAW based on an improved YOLOv8n. Firstly, Omni-dimensional Dynamic Convolution (ODConv) was introduced to enhance the capability of capturing multi-dimensional features and reduce information loss. Secondly, an Asymptotic Feature Pyramid Network (AFPN) was embedded to improve the feature fusion process, enabling direct interaction between non-adjacent level features and effectively alleviating semantic disconnection. Finally, the Wise-IoUv3 loss function with a dynamic non-monotonic focusing mechanism was adopted to optimize bounding box regression, accelerating network convergence while improving detection accuracy. A series of experiments were conducted on the NEU-DET dataset, and the results demonstrated that the modified YOLOv8-ODAW network model outperformed the original network model with a 7.3% increase in mAP at 50% and a 21.95% decrease in computational complexity (GFLOPs). This showcases superior localization and recognition capabilities for steel surface defects while meeting the speed requirements for industrial applications.

Key words:steel;defect detection;YOLOv8n;omni-dimensional dynamic convolution (ODConv);asymptotic feature pyramid network (AFPN);Wise-IoUv3

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Online: November 07,2024
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