An efficient approach for recyclable waste detection and classification using image processing techniques

Abstract

One of the world’s most pressing issues right now is the lack of a competent waste management system, particularly in emerging and underdeveloped countries. Re cycling solid waste, which comprises numerous dangerous non-biodegradable sub stances like glass, metals, plastics, etc., is the most essential step in reducing waste related issues in the environment. Typically, collected waste includes all types of waste that must be thoroughly sorted to recycle efficiently. Most countries use man ual waste sorting techniques, which are efficient. Nevertheless, the waste sorting process by human being is not safe as there is always a risk of exposing them selves to toxic wastes, which could be serious for their health. Our thesis presents a Deep Learning technique based on computer vision for automatically identifying waste. To construct the model, we used Convolutional Neural Networks, real-time object detection systems, such as YOLOv5 and YOLOv7, as well as several trans fer learning-based architectures, including VGG16, MobileNet, Inception-Resnet-v2. The model is trained on numerous images for each type of waste to ensure no overfit ting and greater accuracy. The highest accuracy we achieved for our waste detection model YOLOv5x is 93.7%.