WoodID is a revolutionary project aiming to transform the utilization and reutilization of wood in the industry. Leveraging artificial intelligence (AI) technology, WoodID targets to enhance the circular model of reutilization by operating within a smaller loop, converting wood into various useful products.
The WoodID process commenced with gathering industry insights from Barcelona related to wood, serving as valuable guidance for methodology development. This methodology is delineated into a four-step chain, meticulously defined using extracted guidelines. Steps encompass guideline definition, nesting design modification, dataset collection, and nesting detection utilizing AI algorithms like YOLO and U2Net.
Complementing this process, WoodID employs heuristic techniques for result optimization. This approach integrates constant values such as K – 2 from prior container packing research, enhancing project efficiency and precision.
Data classification stands as a pivotal aspect of WoodID. Different datasets categorize wood by species, type, and defects identification. This precise classification is essential to ensure quality and sustainability throughout all process stages.
Additionally, WoodID benefits from mapping and design technology to streamline the nesting process. Incorporating two additional layers for Warping and Remapping into the U2Net implementation further specialized data, resulting in significant performance improvements.
