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.

Automated waste classification is an essential step toward efficient recycling and waste management. Traditional deep learning models, such as convolutional neural networks, rely on extensive labeled datasets to achieve high accuracy. However, the annotation process is labor-intensive and time-consuming, limiting the scalability of these approaches in real-world applications. Zero-shot learning is a machine learning paradigm that enables a model to recognize and classify objects it has never seen during training by leveraging semantic relationships and external knowledge sources. In this study, we investigate the potential of zero-shot learning for waste classification using two vision-language models: OWL-ViT and OpenCLIP. These models can classify waste without direct exposure to labeled examples by leveraging textual prompts. We apply this approach to the TrashNet dataset, which consists of images of municipal solid waste organized into six distinct categories: cardboard, glass, metal, paper, plastic, and trash. Our experimental results yield an average classification accuracy of 76.30% with Open Clip ViT-L/14-336 model, demonstrating the feasibility of zero-shot learning for waste classification while highlighting challenges in prompt sensitivity and class imbalance. Despite lower accuracy than CNN- and ViT-based classification models, zero-shot learning offers scalability and adaptability by enabling the classification of novel waste categories without retraining. This study underscores the potential of zero-shot learning in automated recycling systems, paving the way for more efficient, scalable, and annotation-free waste classification methodologies.

With the increasing global waste production, there is a rising need for improved waste management solutions to address this growing issue. In the United States, less than 35% of recyclable materials are actually recycled, leading to higher levels of pollution in both soil and oceans. This growing issue has serious environmental repercussions, exacerbating pollution in terrestrial and marine ecosystems. The root of this issue lies in the inefficiencies of the waste sorting procedure, which involves the challenging task of separating materials such as different kinds of plastic and polystyrene from the cluttered waste environment. One way to address this problem is by automatically detecting contamination in recyclable waste before it is compacted in the collection truck hopper. In this paper, we utilize state-of-the-art computer vision-based models to identify contamination within a densely cluttered waste environment. We conduct a comparative analysis with leading object detection models, demonstrating that Vision Transformers achieve superior accuracy.