CPFS-YOLO: a steel surface defect detection method based on cross-stage and multi-scale feature fusion

Abstract

Abstract Steel surface defect detection is a key link to ensure the quality of industrial products and production safety. However, the traditional detection method has some problems, such as low detection efficiency, high false detection rate and high missed detection rate, so it is difficult to adapt to the fine detection needs of modern industry. Therefore, we propose an improved YOLOv11 object detection model named CPFS-YOLO. Firstly, to address the deficiency of traditional detection methods in multi-scale feature extraction, a CSP-partial multi-scale feature aggregation module (CSP_PMSFA) is proposed, which effectively enhances the expression ability of defect features. Secondly, to improve the model’s perception ability for defects of different scales, feature pyramid shared Conv is introduced, enabling the model to achieve more efficient feature extraction without increasing the computational cost. Finally, to optimize the detection performance of minor defects, a small object enhancement pyramid is designed. By fusing global and local information, it enhances the recognition ability of minor defects on the surface of steel materials. Experiments on the NEU-DET dataset show that the [email protected] and [email protected]:0.95 of CPFS-YOLO reach 77.7% and 44.6% respectively, which are 3.6% and 3.2% higher than the baseline model. Compared with existing methods, CPFS-YOLO achieves superior detection accuracy while maintaining a lower computational cost, effectively reducing the problems of false detection and missed detection.