Abstract
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.