One improved YOLOX-s algorithm for lightweight section-steel surface defect detection

Abstract

This study introduces an improved lightweight section-steel surface detection (ILSSD) YOLOX-s algorithm model to enhance feature fusion performance in single-stage target detection networks, addressing the low accuracy in detecting defects on section-steel surfaces and limited computing resources at steel plants. The ILSSD YOLOX-s model is improved by introducing the deep-wise separable convolution (DSC) module to reduce parameter count, a dual parallel attention module for improved feature extraction efficiency, and a weighted feature fusion path using bi-directional feature pyramid network (BiFPN). Additionally, the CIoU loss function is employed for boundary frame regression to enhance prediction accuracy. Based on the NEU-DET dataset, experimental results demonstrate that the ILSSD YOLOX-s algorithm model achieves a 75.9% mean average precision with an IoU threshold of 0.5 (mAP@0.5), an improvement of 7.1 percentage points over the original YOLOX-s model, with a detection speed of 78.4 frames per second (FPS). Its practicality is validated through training and validating it with a lightweight section-steel surface defect dataset from an industrial steel plant, further confirming its viability for industrial defect detection applications.