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<p>Surface defect detection in aluminum profiles remains challenging due to complex textures, illumination variations, reflective noise, and subtle small-scale defects. Conventional YOLO-based detectors rely primarily on spatial-domain features and fail to exploit complementary frequency and gradient information, which is essential for detecting weak, texture-oriented, and elongated defects. This paper proposes <b>Adaptive Triple-Domain YOLOv8</b>, a real-time framework that integrates spatial, frequency (Discrete Wavelet Transform), and gradient (Gabor filter) features within a unified architecture. An adaptive attention-based fusion module dynamically integrates multi-domain features, enabling defect-specific discrimination while preserving high inference speed. Experiments on the Tianchi benchmark demonstrate that the proposed method consistently outperforms spatial-domain, dual-domain, and recent detectors, achieving a mAP@0.5 of 96.7% and a mAP@0.5:0.95 of 73.2% at over 100 FPS. The method significantly improves the detection on low-contrast, texture-dominated, and elongated defects, providing a favorable balance between detection accuracy and real-time efficiency for industrial aluminum surface inspection.</p>

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An efficient triple-domain YOLOv8 for real-time aluminum profile defect detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Mohammad Rostami,
  • Afsaneh Fatemi

摘要

Surface defect detection in aluminum profiles remains challenging due to complex textures, illumination variations, reflective noise, and subtle small-scale defects. Conventional YOLO-based detectors rely primarily on spatial-domain features and fail to exploit complementary frequency and gradient information, which is essential for detecting weak, texture-oriented, and elongated defects. This paper proposes Adaptive Triple-Domain YOLOv8, a real-time framework that integrates spatial, frequency (Discrete Wavelet Transform), and gradient (Gabor filter) features within a unified architecture. An adaptive attention-based fusion module dynamically integrates multi-domain features, enabling defect-specific discrimination while preserving high inference speed. Experiments on the Tianchi benchmark demonstrate that the proposed method consistently outperforms spatial-domain, dual-domain, and recent detectors, achieving a mAP@0.5 of 96.7% and a mAP@0.5:0.95 of 73.2% at over 100 FPS. The method significantly improves the detection on low-contrast, texture-dominated, and elongated defects, providing a favorable balance between detection accuracy and real-time efficiency for industrial aluminum surface inspection.