Lightweight Lswin transformer fusion steel surface model defect detection method research

Fangxu Wu; Yishan Gong

doi:10.1117/12.3035147

19 July 2024 Lightweight Lswin transformer fusion steel surface model defect detection method research

Fangxu Wu, Yishan Gong

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132Q (2024) https://doi.org/10.1117/12.3035147
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

In fact, it is crucial to accurately identify and distinguish steel surface defects in the metal industry. Aiming at the problems of misdetection and poor detection accuracy in scratch and crack defect detection, this paper proposes an algorithm for detecting surface defects on steel based on the LSwin Transformer fusion model. The algorithm takes Mask R-CNN as the baseline framework, fuses it with the improved Swin Transformer backbone network, combines the feature extraction capability of convolutional neural network with the global dependency construction capability of Swin Transformer, and finally introduces the multi-head local window offset (LS-Tr) module to detect steel surface defects by means of a lightweight PAN and Mask Switch module (MSM) to detect steel surface defects, thus reducing the number of model parameters and increasing the inference speed. Experimental validation on the NEU-DET dataset shows that the accuracy of the algorithm in detecting scratched (SC) and cracked (Cr) defects is improved by 29.8% and 6.9%, respectively, and the accuracy in detecting the mAP value reaches 82.5%, which is 7.1% higher than that before the improvement. It is better than the current mainstream steel surface defect detection algorithms and meets the needs of steel surface defect detection tasks.

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

Citation Download Citation

Fangxu Wu and Yishan Gong "Lightweight Lswin transformer fusion steel surface model defect detection method research", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132Q (19 July 2024); https://doi.org/10.1117/12.3035147

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KEYWORDS

Transformers

Defect detection

Data modeling

Performance modeling

Target detection

Detection and tracking algorithms

Feature extraction

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