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This paper is based on the research process of applying the YOLO model to identify and classify waste. In the self-collected dataset, we tested 2 versions: YOLOv11 and YOLOv12. The test results show that two models have a reasonably high average accuracy of about 71–72% mAP50-95 to identify and classify waste types in the test dataset, a low error rate, and stable performance. Through the research, the reader can see the great uses of artificial intelligence in general and the YOLO model in particular in the field of waste identification and classification. Therefore, this research can become a reference for future research on the application of computer vision in image identification and classification.

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DeepWaste: Deep Learning-Based Waste Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Duc-Kien Bui,
  • Tien-Thanh Do,
  • Ngoc-Thanh Pham,
  • Ngoc-Anh Le,
  • Kim-Thai Dinh,
  • Phuong-Thao Le,
  • Phuong-Thao Nguyen,
  • Minh-Quan Vu,
  • Phuong Anh Nguyen

摘要

This paper is based on the research process of applying the YOLO model to identify and classify waste. In the self-collected dataset, we tested 2 versions: YOLOv11 and YOLOv12. The test results show that two models have a reasonably high average accuracy of about 71–72% mAP50-95 to identify and classify waste types in the test dataset, a low error rate, and stable performance. Through the research, the reader can see the great uses of artificial intelligence in general and the YOLO model in particular in the field of waste identification and classification. Therefore, this research can become a reference for future research on the application of computer vision in image identification and classification.