Automatic object detection for disassembly and recycling of electronic board components

This paper presents the development of a deep learning-based object recognition system designed to automate and speeding up the disassembly process of electrical and electronic components. The main goal is to address the mounting global issue of Waste Electrical and Electronic Equipment (WEEE) management. The increasing availability of technology and the expansion of consumer markets have led to a significant surge in the generation of WEEE, necessitating the urgent development of sustainable and automated strategies for its disposal and resource recovery. Traditional manual disposal methods and the uncontrolled accumulation of WEEE can pose serious threats to the environment, human health and natural resources. A comprehensive approach, involving advanced recycling technologies, life-cycle management and policy reforms is required to handle this escalating waste stream. The complexity of WEEE management is heightened by the diversity of product design and composition, making efficient material selection processes often labour-intensive and expensive. This work focuses on the automated detection and sorting of WEEE products. The proposed system enables rapid identification of products and components, in order to facilitate the subsequent disassembly and reuse of components that are still considered functional, safe, and of good quality. This concept is illustrated through a case study where recognition of six different electronic boards is performed.