Research on an improved YOLOv7 tiny steel defect detection algorithm

Ling Wang; Yanfeng Li; Hongru You; Peng Wang

doi:10.1117/12.3048064

3 October 2024 Research on an improved YOLOv7 tiny steel defect detection algorithm

Ling Wang, Yanfeng Li, Hongru You, Peng Wang

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Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720L (2024) https://doi.org/10.1117/12.3048064
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

General defect detection methods perform well in detecting obvious defects, but detecting subtle defects such as scratches and cracks in steel still poses a challenge. This paper proposes a steel defect detection algorithm based on the YOLOv7-tiny model[1], and names as CaGFPN-YOLO. Firstly, a CA module is introduced into the feature extraction network to enhance the model's ability to extract subtle defect features; Subsequently, the neck structure of the YOLOv7-tiny model is replaced with an improved RepGFPN structure to fully integrate multi-scale features, further enhancing the model's ability to detect subtle defects. Experiments conducted on the Northeastern University hot-rolled steel surface defect dataset show that the CaGFPN-YOLO model improves the mAP@0.5 by 3.1%, recall rate by 3.6%, and F1 score by 3.5% compared to the YOLOv7-tiny model, the CaGFPN-YOLO model can effectively enhance the detection capability of steel defects.

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

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Ling Wang, Yanfeng Li, Hongru You, and Peng Wang "Research on an improved YOLOv7 tiny steel defect detection algorithm", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720L (3 October 2024); https://doi.org/10.1117/12.3048064

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KEYWORDS

Defect detection

Performance modeling

Neck

Feature extraction

Data modeling

Feature fusion

Object detection

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