FCS-YOLO: an efficient algorithm for detecting steel surface defects

Abstract

Due to the imperfection of the manufacturing process and external factors, there may be some defects on the steel surface, which seriously affects the life and availability of the steel. Aiming at the problems of low precision and poor real-time performance in the inspection of steel surface defects in the past, an algorithm named FCS-you only look once (YOLO) was proposed for the inspection of steel surface defects. Firstly, the backbone feature extraction network is reconstructed using FasterNet to reduce the number of model parameters and weight size. Secondly, a new C2f-global attention mechanism module is proposed, which can focus image features more accurately by dynamically adjusting feature response. Finally, the attention mechanism SK is embedded in the feature extraction network to adjust the receptive field of the model, strengthen its feature recognition ability, and effectively improve the detection accuracy of steel surface defects. The test results show that FCS-YOLO has the best detection effect and the fastest detection speed among the comparison models. On the NEU-DET Dataset, the detection accuracy reached 81.8% and the detection speed reached 129.87 frames per second, which met the requirements of accurate and fast detection of strip surface defects.