RESUMEN
Every year human discharges about 350 million tons of plastic waste into the environment and can be projected to triple in 2060 without any attempts to change situation. From 1970 to 2019, an estimation of 130 million tons of plastic waste was accumulated into the rivers, lakes and sea, while only 27 % is recycled and utilized. Moreover, waste treatment plants in most places around the world are using out-of-date technology, may pose a threat to the health of the workers. Therefore, it is essential to modernize these systems for protecting human health. This paper proposes fine-tuning DETR, which applies Artificial Intelligent in plastic waste sorting system. Consequently, this study analyzed the applicability of fine-tuning DETR in the domain of plastic waste categorization and its potential drawbacks. For fair experiment and evaluation, model candidates were trained and evaluated on an industrial plastic waste dataset. The fine-tuning DETR outperformed other candidates in the context of critical indicators, from accuracy (25.1 mAP), processing speed (28 FPS) to computational cost (GFLOPs 86). Furthermore, fine-tuning DETR possesses the capability of autonomous operation without requiring human intervention, distinguishing this candidate from other prevalent algorithms. Our research demonstrates that, fine-tuning DETR specifically and Transformer-based algorithms in general, are entirely suitable and hold significant potential for large-scale application in holistic plastic waste sorting systems.