Advancing e-waste classification with customizable YOLO based deep learning models

The burgeoning problem of electronic waste (e-waste) management necessitates sophisticated, efficient, and precise classification techniques for recycling and repurposing. To address these critical environmental and health implications, this research delves into a comprehensive analysis of three cutting-edge object detection models: YOLOv5, YOLOv7, and YOLOv8. These models are examined through the lens of efficient e-waste classification, a pivotal step in recycling and repurposing efforts. The 'You Only Look Once' (YOLO) methodology underpins our research, highlighting the distinctive architectural features of each model, including the CSPDarknet53 backbone, PANet, and advanced anchor-free detection. This research approach involved the creation of a specialized image dataset encompassing seven distinct e-waste categories to facilitate the training and validation of these models. The performance of improved and customizable YOLOv5, YOLOv7, and YOLOv8 was meticulously evaluated across various parameters such as precision, recall, speed, and training efficiency. This evaluation explores the architectural nuances of each model and its efficacy in accurately detecting diverse e-waste components. The standout performer, YOLOv8, demonstrated exceptional capabilities with its enhanced feature pyramid networks and improved CSPDarknet53 backbone with 53 convolutional layers, achieving superior precision and accuracy. Notably, this model showcased a significant reduction in training time while leveraging the computational power of the Tesla T4 GPU on Google Colab. However, the research also identified challenges, particularly in object orientation detection, suggesting avenues for future refinement. This study underscores the vital role of advanced YOLO architectures in e-waste management, providing critical insights into their practical viability, applicability in real-world scenarios, and potential limitations. By setting a benchmark in real-time object detection, our work paves the way for future innovations and improvements in environmental management technologies, specifically tailored to meet the escalating challenge of e-waste management.