Lightweight Lswin transformer fusion steel surface model defect detection method research

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.