SGCDT-YOLO: A multi-scale feature fusion network with content-aware selective mechanisms for wood surface defect detection

Wood surface defect detection is plagued by challenges in multi-scale feature fusion, small object recognition, and other aspects. To address these issues, this paper proposes SGCDT-YOLO, an improved YOLOv11n algorithm integrated with the Selective Weighted Spatial Reduction (SWSR), Guided Expansion and Dimensional-Oriented Selective Fusion Enhancement Network (GEDOSFEN), Dual-Stage Gradient Receptive Field Aggregation (DSGRFA), and triplet attention modules. A dual-branch SWSR module is designed to enable content-aware downsampling, preserving discriminative information in critical defect regions while suppressing irrelevant areas. GEDOSFEN is proposed to build bidirectional diffusion paths via intermediate-scale features, facilitating effective interaction between deep semantic information and shallow texture details across detection scales. Inspired by dilated-wise residual and dilated re-param block, the DSGRFA module is designed to decompose receptive field expansion into regional feature construction and semantic filtering, extending the perceptual range with low computational overhead. Triplet attention is incorporated to establish cross-dimensional correlations among channel, height, and width, enhancing the unified perception of multi-morphology defects. Experimental results show that SGCDT-YOLO achieves 78.12% [email protected] and 40.42% [email protected]:0.95, representing improvements of 13.41% and 5.43% over the baseline, respectively, with precision at 76.62% (+10.25%), recall at 68.52% (+6.76%), and F1-score at 72.34% (+8.36%).Meanwhile, the number of parameters is reduced by 10.47% compared with the baseline model. Comparative tests reveal a 12.22–16.02% [email protected] improvement over mainstream algorithms. Visualization analyses of inference results, heatmaps, and feature maps confirm the algorithm’s high accuracy in defect localization and classification.