CABF-YOLO: a precise and efficient deep learning method for defect detection on strip steel surface

Abstract

Deep learning algorithms have gained widespread usage in defect detection systems. However, existing methods are not satisfied for large-scale applications on surface defect detection of strip steel. In this paper, we propose a precise and efficient detection model, named CABF-YOLO, based on the YOLOX for strip steel surface defects. Firstly, we introduce the Triplet Convolutional Coordinate Attention (TCCA) module in the backbone of the YOLOX. By factorizing the pooling operation, the TCCA module can accurately capture cross-channel features to identify the location information of defects. Secondly, we design a novel Bidirectional Fusion (BF) strategy in the neck of the YOLOX. The BF strategy enhances the fusion of low-level and high-level semantic information to obtain fine-grained information. Lastly, the original bounding box loss function is replaced by the EIoU loss function. In the EIoU loss function, the penalty term is redefined to consider the overlap area, central point, and side length of the required regressions to accelerate the convergence rate and localization accuracy. On the benchmark NEU-DET dataset and GC10-DET dataset, the experimental results show that the CABF-YOLO achieves superior performance compared with other comparison models and satisfies the real-time detection requirement of industrial production.