Abstract
Wood is an invaluable natural resource that has been used throughout human history in a variety of industries. Because of its adaptability, durability, and visual appeal, it is widely used in building, furniture, paper, and several other industries. However, wood’s integrity and quality are crucial for its industrial uses. In order to direct the KUKA robotic arm for flaw identification, this article suggests a convolutional neural network model for wood inspection. YOLOv5, YOLOv5-tiny, and YOLOv7—three cutting-edge object identification algorithms—were tested on a collection of wood images to validate this method. YOLOv7 showed better performance when compared to previous versions. To create wood orientation estimation, YOLOv7 was selected because of its exceptional performance. This strategy may increase wood processing efficiency and address issues the industry has, such as labor shortages and adherence to natural product safety and quality regulations.
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Afaf, S., Abou El Kalam, A., Bouslimani, Y. (2024). Wood Surface Defects Detection Based on AI Algorithm Yolov7. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-031-54288-6_2
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DOI: https://doi.org/10.1007/978-3-031-54288-6_2
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