Rtsds:a real-time and efficient method for detecting surface defects in strip steel

Abstract

To address the issues of varying defect sizes, inconsistent data quality, and real-time detection challenges in steel defect detection, we propose a real-time efficient steel defect detection network (RTSD). This model employs a multi-scale feature extraction module (MSC3) and a mid-sized object detector (MidObj) to comprehensively capture texture features of defects across different scales. We incorporate a coordinate attention module (CA) and replace the spatial pyramid pooling structure (SPPF) to enhance defect localization capabilities. Additionally, we introduce the Wise-IoU (WIoU) loss function to balance attention to various quality defects. To address the real-time detection issue, we use Taylor channel pruning to reduce model complexity and employ channel-wise knowledge distillation instead of fine-tuning to mitigate the negative impacts of pruning. Experimental results show that on the NEU-DET data set, the average precision of RTSD reaches 83.5%. The model parameters, calculation amount, and size are 5.9M, 7.9 GFLOPs, and 11.9M, respectively, with an inference speed of up to 247.6 FPS. This demonstrates that our method can enhance performance while significantly reducing model complexity and computational overhead, offering a highly practical solution for industrial applications.