ACT-YOLO: An Efficient Multi-Module Fusion Object Detection Algorithm for Steel Surface Defect Detection

Abstract

With the development of intelligent manufacturing, higher requirements for real-time performance and accuracy have been placed on the inspection of surface quality in industrial products. As a core basic material in manufacturing, steel has various complex surface defects, such as cracks, scratches, and scale, which are characterised by small size, diverse shapes, and strong background interference, posing significant challenges for automated inspection. To address the issues of insufficient detection accuracy, limited feature fusion capabilities, and coupling of classification and localisation tasks in existing YOLO models when processing fine-grained defects on steel surfaces, this paper proposes a high-performance object detection algorithm based on an improved YOLOv8m: ACT-YOLO (Adaptive Content-guided Task-aligned YOLO). This algorithm integrates three key modules: the AFMA module to enhance multi-scale perception capabilities for small objects; the CGAF module to achieve content-guided multi-attention feature fusion; and the TADD module to optimise the dynamic alignment between classification and regression tasks in the detection head. Evaluations on the NEU-DET steel surface defect benchmark dataset demonstrate that ACT-YOLO achieves an mAP@0.5 of 86.4% and a detection speed of 115 FPS. Compared to non-YOLO methods such as SSD (mAP@0.5: 61.0%, FPS: 41), RetinaNet (mAP@0.5: 69.5%, FPS: 15), and RT-DETR-r101 (mAP@0.5: 78.8%, FPS: 108), as well as other YOLO series models, ACT-YOLO exhibits significant advantages in both detection accuracy and real-time performance. Generalisation experiments on the GC10-DET dataset also validate its cross-scenario adaptability. ACT-YOLO balances detection accuracy, speed, and model lightweighting, making it suitable for the demand for efficient, real-time defect detection systems in actual industrial environments, with broad engineering application prospects and research value.