CMH‑Net:a structured and optimized network for real-time steel surface defect detection

Steel surface defect detection plays a pivotal role in modern industry, ensuring product quality and safety. However, significant challenges still remain, such as unstructured nature and multi-scale characteristics of defects. One of the key concerns is the balance between detection accuracy and real-time performance. To address the above challenges, this paper introduces CMH-Net, a lightweight one-stage detection model based on YOLOv12. CMH-Net is designed with several efficient modules that significantly enhance detection ability. The backbone incorporates a two-stage cascaded Haar wavelet downsampling module to improve unstructured feature extraction. Furthermore, a multi-scale selective Mamba-Like Linear Attention(MLLA) module is proposed for multi-scale feature fusion, improving detection accuracy for defects of varying scales. CMH-Net comprises and optimizes two structural modules with few parameters, ultimately enhancing feature representation, improving overall model performance and efficiency. Following this, a hybrid loss function is applied to enhance bounding box accuracy and model robustness. Experimental results on two public datasets for steel surface defect detection demonstrate that the prposed CMH-Net outperforms state-of-the-art methods in terms of the mAP50 metric (NEU-DET: 0.814, GC10-DET: 0.715). With only 3.6M parameters and 6.4ms inference time, CMH-Net achieves an optimal trade-off between detection accuracy and real-time efficiency.