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

Authors

Mohammad Rostami, Afsaneh Fatemi

Published in

Scientific reports. Jun 17, 2026. Epub Jun 17, 2026.

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 [email protected] of 96.7% and a [email protected]: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.

PMID:
42310421
Bibliographic data and abstract were imported from PubMed on 18 Jun 2026.

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