YOLO-RMF: A Real-Time Surface Defect Detection Model for Hot-Rolled Strip Steel with Reparameterized Convolution and Axial Attention

Abstract

Hot-rolled strip steel surface defect detection is a vital industrial task that demands both high precision and real-time inference. Nevertheless, existing deep-learning-based detectors struggle to balance accuracy and speed, limiting their practical deployment on resource-constrained edge devices within harsh manufacturing scenarios. To address this gap, this paper develops YOLO-RMF, a real-time one-stage detector for hot-rolled strip steel inspection. Four core components are presented: a reparameterized 2 × 2 convolution (RPRC2) for enhanced feature extraction with lower inference computational cost than standard 3 × 3 convolutions, a two-dimensional axial attention (TDAA) module to capture long-range spatial dependencies, an adaptive-weighted multi-scale feature fusion pyramid network (MSF-FPN), and a hybrid adaptive pruning strategy combining BN scaling factors and feature-map rank analysis for model compression. Evaluated on NEU-DET and MVIT datasets, YOLO-RMF obtains 76.1% mAP50 and 40.6% mAP50-95 at 212 FPS (batch size 1, RTX 4090 GPU), achieving a favorable balance between detection sensitivity and precise localization for edge-computing-based industrial inspection. The pruning scheme reduces parameters by 36.3% and GFLOPs by 49.0% with minor accuracy loss. Deployed on Jetson AGX Orin, the pruned model reaches 169 FPS under FP16 and 233 FPS under INT8 quantization. Experimental outcomes show that YOLO-RMF achieves a favorable accuracy-speed trade-off and is suitable for practical industrial edge-based defect inspection scenarios.