Deep Learning-Based Faulty Wood Detection with Area Attention

Improving consumer satisfaction with the appearance and surface quality of wood-based products requires inspection methods that are both accurate and efficient. The adoption of artificial intelligence (AI) for surface evaluation has emerged as a promising solution. Since the visual appeal of wooden products directly impacts their market value and overall business success, effective quality control is crucial. However, conventional inspection techniques often fail to meet performance requirements due to limited accuracy and slow processing times. To address these shortcomings, the authors propose a real-time deep learning-based system for evaluating surface appearance quality. The method integrates object detection and classification within an area attention framework and leverages R-ELAN for advanced fine-tuning. This architecture supports precise identification and classification of multiple objects, even under ambiguous or visually complex conditions. Furthermore, the model is computationally efficient and well-suited to moderate or domain-specific datasets commonly found in industrial inspection tasks. Experimental validation on the Zenodo dataset shows that the model achieves an average precision (AP) of 60.6%, outperforming the current state-of-the-art YOLOv12 model (55.3%), with a fast inference time of approximately 70 milliseconds. These results underscore the potential of AI-powered methods to enhance surface quality inspection in the wood manufacturing sector.