Surface defect detection in aluminum profiles is critical for maintaining product quality and ensuring efficient industrial production. However, existing detection algorithms often struggle to address the challenges of imbalanced defect categories, low detection accuracy for small-scale defects, and irregular flaw geometries. These limitations compromise both detection accuracy and algorithmic robustness. Accordingly, we proposed MCH-YOLOv12—an improved YOLOv12-based algorithm for precise defect detection. Firstly, we enhanced the original Ghost convolution by incorporating multi-scale feature extraction and named the improved version MultiScaleGhost, which replaced the standard convolutions in the Backbone of YOLOv12. This improvement mitigated the limitations of single-scale convolution, enhancing feature representation and the detection of irregularly shaped defects. Secondly, we addressed the directional and edge-specific nature of defects by enhancing the traditional Channel-wise Gated Linear Unit (CGLU). We proposed the Spatial-Channel Collaborative Gated Linear Unit (SCCGLU), which was embedded after the C3k2 module in the Neck of YOLOv12 to better capture fine-grained features. Finally, we designed a Hybrid Head combining anchor-based and anchor-free detection to improve adaptability to defects of various sizes and shapes. Experimental results on an aluminum profile defect dataset demonstrated improved accuracy, reduced category imbalance, and lower parameters and Floating Point Operations (FLOPs), making the algorithm suitable for real-time industrial inspection.
Sun et al. (Mon,) studied this question.
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