YOLOv11–BiFPN–DAAF: An Object Detection Framework for Automated Surface Inspection of Balsa Wood Panels

Automated surface inspection of balsa wood panels is challenging because defects may be small, elongated, weakly contrasted, or visually similar to natural grain patterns. This study proposes YOLOv11–BiFPN–DAAF, an enhanced object-detection architecture that combines bidirectional multi-scale feature fusion with adaptive dual-attention feature refinement. The task was formulated as single-class detection, with all anomalous surface regions labeled as Defect. The dataset comprised 508 manually annotated RGB images, including independent internal and external production test sets. A preliminary screening identified YOLOv11-m512 as the reference configuration, followed by a controlled 2×2 factorial ablation comprising the baseline, BiFPN, DAAF, and their combined integration. Each configuration was trained using five independent random seeds under identical experimental conditions. On the validation set, the combined architecture achieved a precision of 0.893±0.004, recall of 0.848±0.006, mAP@0.5 of 0.897±0.004, and mAP@0.5:0.95 of 0.389±0.004. Relative to the baseline, the largest improvement was obtained for mAP@0.5:0.95, with a relative gain of 9.89%, indicating improved localization under stricter IoU thresholds. The improvement was retained on the independent internal test set, where the proposed architecture reached mAP@0.5 and mAP@0.5:0.95 values of 0.892±0.005 and 0.384±0.006, respectively. On the external production test set, the corresponding values were 0.865±0.007 and 0.358±0.008, representing absolute improvements of 0.034 and 0.042 over the original YOLOv11 baseline. Under the matched experimental protocol, YOLOv11–BiFPN–DAAF also achieved the highest principal detection metrics among the evaluated representative detectors. Although BiFPN and DAAF introduced a moderate computational overhead, the architecture maintained an inference time of 9.6±0.3 ms per image. These findings support the potential of the proposed architecture for automated balsa wood panel inspection, while broader multi-site and hardware-level validation remains necessary before large-scale industrial deployment.