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 $2\times 2$ 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 $0.893\pm 0.004$, recall of $0.848\pm 0.006$, mAP@0.5 of $0.897\pm 0.004$, and mAP@0.5:0.95 of $0.389\pm 0.004$. 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 $0.892\pm 0.005$ and $0.384\pm 0.006$, respectively. On the external production test set, the corresponding values were $0.865\pm 0.007$ and $0.358\pm 0.008$, 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 $9.6\pm 0.3$ 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.

Este libro presenta una innovadora propuesta basada en visión artificial para detectar de forma automatizada defectos superficiales en la madera de balsa, un recurso vital en múltiples sectores productivos. A través de una rigurosa investigación que combina fundamentos teóricos y pruebas prácticas en entornos industriales reales, se expone un sistema capaz de optimizar el control de calidad, reduciendo el margen de error humano y mejorando la trazabilidad del proceso productivo.

Balsa wood defects can resemble natural grain, making successful detection insufficient to establish precise localization or robustness to acquisition changes. This study investigates these limitations using the previously published YOLOv11–BiFPN–DAAF framework as a reference for analyzing adaptive multiscale attention. On a 508-image industrial dataset, five training seeds support selected-IoU evaluation, geometric box-error analysis, stratification by defect size, contrast, and elongation, and controlled comparisons of within-scale attention and mixing operators. Robustness is assessed under 15 brightness, contrast, noise, blur, and compression conditions and an external production campaign. Relative to YOLOv11, the complete framework increases internal-test AP from 0.865 to 0.892 at IoU 0.50, from 0.385 to 0.442 at IoU 0.75, and from 0.082 to 0.125 at IoU 0.90, while reducing mean geometric errors among matched detections. It improves mAP@0.5:0.95 by 0.035 (paired-seed 95% CI: 0.03061–0.03939) and exhibits smaller mean degradation under every tested perturbation, while external mAP@0.5:0.95 decreases from the internal value of 0.384 to 0.358. In the within-scale ablation, learned gating exceeds fixed mixing by 0.003 in mean mAP@0.5:0.95 (0.384 versus 0.381), with additional trainable capacity. Composite spatial-attention maps yield a concentration ratio of $4.8\pm 0.7$, indicating higher mean weights inside annotation boxes without establishing a causal suppression mechanism. Paired statistical analyses distinguish training-seed variability from test-panel uncertainty. The findings identify localization gains, acquisition sensitivities, and the observed contribution of adaptive refinement within the established architecture. They support further evaluation of difficult defects and independent production domains rather than unrestricted generalization claims.

Designing and implementing a preventive maintenance plan in the Ecuadorian mining industry requires systematic methodologies to reduce failures in crushing and grinding machines, improving operational availability and productivity. The main problem identified is reliance on corrective maintenance, with no historical failure records, which causes unexpected shutdowns. This study applies the FMEA (Failure Mode and Effects Analysis) methodology to evaluate the criticality of equipment using the Risk Priority Number (NPR), complemented with the Weibull distribution to model useful lifetimes. Key equipment such as the ball mill and jaw crusher at a mining beneficiation plant were analyzed. The results indicate that 25% of the subcomponents have NPR > 300 (unacceptable risk), mainly concentrated in structural elements subjected to severe operating conditions. A scenario analysis based on the fitted Weibull models and on reductions reported in the literature projects that implementation of the proposed preventive plan would reduce unplanned shutdowns by 40–50%, raising operational availability from the current 73% to approximately 90%; these figures are model-based projections and not post-implementation measurements. Weibull integration provides a robust statistical framework for the transition from reactive maintenance to predictive schemes, optimizing intervention intervals and extending the life of critical assets.

Surface defects in balsa wood panels can compromise visual quality, mechanical reliability, and industrial acceptance, especially in applications where lightweight wood materials are used under strict quality requirements. Manual inspection remains common in balsa wood processing; however, it is time-consuming, subjective, and prone to human error. This study designed and evaluated a YOLOv11-based deep learning detection architecture adapted to automated surface defect inspection in balsa wood panels. A custom image dataset was constructed from real panel surfaces acquired under industrial inspection conditions, and all visible surface failures were consolidated into a single target class named Defect. The images were annotated using Label Studio and adapted to the YOLOv11 detection format. Seven YOLOv11 configurations were systematically evaluated by varying model scale, input image resolution, number of epochs, batch size, initial learning rate, and optimizer strategy. The experimental results showed that YOLOv11_m512 achieved the best overall performance, with a precision of 0.829, recall of 0.889, mAP@0.5 of 0.870, and mAP@0.5:0.95 of 0.354, while maintaining a model size of 38.61 MB and an inference time of 34.09 ms per image. The comparative analysis demonstrated that increasing image resolution alone did not improve detection performance, as high-resolution AdamW-based configurations showed lower mAP values and higher inference times. Instead, the best results were obtained by balancing backbone capacity, input resolution, optimizer strategy, and batch size. Qualitative inference results confirmed that the proposed model can detect cracks, stains, knots, splits, and localized discontinuities under heterogeneous wood grain and illumination conditions. The findings support the feasibility of integrating YOLOv11-based computer vision into automated quality-control systems for balsa wood panel inspection, providing a more objective and consistent alternative to manual inspection.