Wood surface crack detection is crucial for automated quality inspection in wood processing, yet remains challenging in real production lines due to thin and low-contrast crack patterns, strong interference from repetitive wood-grain textures, and pronounced appearance shifts across wood species and illumination conditions. To address these issues, we propose TDDC-YOLO, a robust crack detection framework built upon YOLOv8. The core idea is to explicitly disentangle texture and defect representations via an orthogonality-constrained decoupling component, thereby suppressing texture-induced false activations. Meanwhile, we introduce a lightweight crack-geometry guidance branch with readily constructible pseudo labels to enhance the structural consistency of slender cracks, and further improve robustness under domain shifts using a frequency-aware mixing strategy with matched-positive consistency regularization. Experiments on the evaluated wood-surface crack dataset show that TDDC-YOLO improves detection accuracy with limited additional overhead, reaching Formula: see text = 0.938 and Formula: see text = 0.673 on the test set while maintaining real-time inference on our test platform (3.9 ms/image, 256 FPS). Moreover, cross-wood evaluations on two species (Ash and Bubinga) indicate improved robustness to wood-species induced texture shifts (e.g., Ash-to-Bubinga Formula: see text from 0.575 to 0.615), suggesting its potential for more stable detection under the evaluated cross-wood setting.
Yan Chen (2026) studied this question.