| Summary: | Wood surface detection is a process of identifying and locating wooden surfaces in an image or video using computer vision techniques. This technique can be used in various applications such as furniture manufacturing, construction, and lumber mills. Wood defect detection is an important task in the wood industry as it ensures the quality of wood products. In this study, YOLOv4-tiny algorithm is proposed to detect seven types of wood defects from wood texture images. Analysis was performed by applying several detection models to detect the wood defects. The performance of the proposed model is evaluated in terms of precision, recall, F1-score, and mAP. The algorithm has the potential to improve the efficiency and quality of the wood industry and could be applied to other similar tasks, such as identifying defects in other materials and products. © 2023 IEEE.
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