Research on hot rolled steel strip defects image processing method based on multi-scale detection

ZeHui Yun; JingGuo Lv; Jing Zhang; YiTing Liu

doi:10.1117/12.2667414

10 February 2023 Research on hot rolled steel strip defects image processing method based on multi-scale detection

ZeHui Yun, JingGuo Lv, Jing Zhang, YiTing Liu

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125522J (2023) https://doi.org/10.1117/12.2667414
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

Hot rolled steel strip is usually detected by image processing for defects, but it is often affected by light, and conventional image processing methods cannot effectively detect defects with small areas. In this paper, an image processing method is proposed to overcome the effect of fill light for the detection of steel strip surface damage by convolutional neural network multiscale detection method. In the target detection part, K-means++ clustering was performed on the anchor size, while the S-SPP module for multi-scale detection was introduced to further improve the detection of small-area damage. After training the model with the NEU-CLS dataset, the accuracy reached 94.6% and the detection speed was 27.3 ms. Through comparison experiments, the experimental results show that the proposed method of image processing can detect steel strip damage more accurately and faster, which provides a feasible method for practical application in factory scenes.

Citation Download Citation

ZeHui Yun, JingGuo Lv, Jing Zhang, and YiTing Liu "Research on hot rolled steel strip defects image processing method based on multi-scale detection", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125522J (10 February 2023); https://doi.org/10.1117/12.2667414

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KEYWORDS

Image processing

Defect detection

Target detection

Convolution

Small targets

Data modeling

Feature extraction

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