MCM-YOLO: A Lightweight Conflict Mitigation Network for Industrial Metal Surface Defect Detection

Industrial metal surface defect detection is essential for visual sensing-based quality inspection, where lightweight models must balance reliable defect perception and efficient deployment under limited computational resources. However, weak textures, blurred boundaries, and small defect scales often reduce the reliability of lightweight detectors, while heavy global modeling modules increase computational cost. This article proposes a lightweight You Only Look Once (YOLO)-based framework, termed Mediation-based Conflict Mitigation YOLO (MCM-YOLO), for industrial metal surface defect detection. Built upon YOLOv11, MCM-YOLO introduces a C3-Res2Lite (C3-R2L) block to enhance fine-grained local representation and an Aggregated Bidirectional Feature Pyramid Network (Agg-BiFPN) to strengthen multiscale feature aggregation. We further observe that directly coupling the enhanced backbone and strengthened neck may degrade performance, which is referred to as Feature Integration Conflict (FIC). To alleviate the potential feature incompatibility associated with FIC, this article introduces a synergistic attention block, termed MCM-SAB, at the critical interface to perform coordinated channel recalibration and spatial refinement. Experiments on NEU-DET and GC-10 show that MCM-YOLO achieves 80.4% and 65.1% mean average precision (mAP) at an intersection-over-union (IoU) threshold of 0.5, respectively, with 6.3 giga floating-point operations (GFLOPs) and 2.70 million parameters. These results indicate that MCM-YOLO provides a competitive accuracy-efficiency tradeoff for industrial visual inspection.