Abstract: This review paper focuses on the application of object detection techniques in e-waste management. Electronic waste (e-waste) poses environmental and health risks, necessitating efficient handling and disposal methods. Traditional approaches face challenges, highlighting the need for automated solutions. The paper explores the role of computer vision and object detection algorithms in identifying and categorizing e-waste items. Various techniques of deep learning models, are examined for their effectiveness in e-waste object detection. Advantages, such as increased accuracy and efficiency, are discussed. Additionally, the paper briefly touches upon the potential benefits of integrating object detection with robotic arm systems for enhanced e-wa ste separation processes. The review provides insight into current research advancements and highlights future prospects for object detection in large-scale e-waste management. These technologies offer promising avenues for automating e-waste identification and improving the overall efficiency of e-waste management systems.
Keywords: E-Waste,Object Detection,Robotic arm
|
DOI:
10.17148/IJARCCE.2023.126118
