Steel surface defect detection based on YOLO V8

Minzheng Li; Hao Dong

doi:10.1117/12.3037883

12 September 2024 Steel surface defect detection based on YOLO V8

Minzheng Li, Hao Dong

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 1325627 (2024) https://doi.org/10.1117/12.3037883
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

The detection of surface defects in steel is a crucial step in ensuring the quality of the steel. Traditional defect detection methods suffer from low accuracy. The complex shapes of steel surface defects and generally small target areas significantly affect the accuracy of steel surface defect detection. To address these issues, this paper proposes an improved steel surface defect detection algorithm based on a target detection model. This is achieved by incorporating a lightweight backbone network, a Prior Attention Mechanism module (CPCA), and redesigning the Neck module to enhance the accuracy of steel defect detection. Finally, the proposed defect detection algorithm is validated using the publicly available steel surface defect detection dataset NEU-DET. Experimental results show that the network model proposed in this paper has good detection accuracy, with an average precision of 79.1%, which is a 5% improvement over the original algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Minzheng Li and Hao Dong "Steel surface defect detection based on YOLO V8", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 1325627 (12 September 2024); https://doi.org/10.1117/12.3037883

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KEYWORDS

Defect detection

Feature extraction

Convolution

Detection and tracking algorithms

Feature fusion

Object detection

Performance modeling

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