Contamination Detection From Highly Cluttered Waste Scenes Using Computer Vision

Abstract

As the global production of waste continues to rise, there is a growing demand for more effective waste management strategies to handle this expanding problem. Recycling rates in the United States for recyclable materials are below 35%, resulting in elevated levels of waste being sent to landfills. This situation has alarming consequences, contributing to rising pollution in both soil and aquatic ecosystems, and is a significant source of concern for environmental scientists and the general population alike. The presence of contamination in recycling collection trucks is a root cause impacting recycling rates, leading to the rejection of entire loads from recycling processing sites. This problem can be alleviated by automatically detecting contamination in recyclable waste that is loaded into the collection truck hopper before compaction. In this paper, we have used different state-of-the-art computer vision-based models such as Faster-RCNN, Cascade-RCNN, Retinanet, YOLOv8 and Mask-RCNN to identify contamination within a densely cluttered waste environment. We further investigate the viability of transfer learning, comparing it to the models trained from scratch. The YOLOv8-x model attained a mean-average precision of 0.395 without using transfer learning, whereas with the incorporation of transfer learning, its performance increased to 0.463.

Keywords

• Transfer learning
• computer vision
• waste management
• instance segmentation
• object detection
• supervised learning