Development of Computer Vision Algorithms for Multi-class Waste Segregation and Their Analysis

Classification of waste for recycling has been a focal point for scientists interested in the field of conservation of the environment. Recycling consists of numerous steps, of which one of the most crucial is the segregation of recyclables from all other waste. Due to a lack of safety standards in developing countries, waste collection is often done manually by domestic helpers, or "rag-pickers". Such a process risks individual and public health. The waste collection methods may ultimately cause waste to become non-recyclable due to cross-contamination. Literature shows that research in this direction focuses on a single class of waste detection. The proposed work investigates CNN, YOLO, and faster RCNN-based multi-class classification methods to detect different types of waste at the collecting point. The smart dustbin proposed employs these computer vision methods with a Raspberry Pi microcontroller and camera module. The experimental results for multi-class classification show that the CNN has 80% of accuracy with 60% of the loss. Whereas the YOLO algorithm shows an accuracy of 88% and a loss of 40%. But the best results were obtained from faster RCNN object detection with API, with an accuracy of 91% and a loss of 16%. There is already an existing method for making a smart dustbin, so the results are compared to show how computer vision can be used to make a smart dustbin. This shows how computer vision can be used to make a smart dustbin.