The Computer Vision System for Automated Electronic Parts Recognition aims to develop an intelligent system capable of recognizing and classifying various electronic components from images. The system uses advanced deep learning techniques, specifically the YOLO (You Only Look Once) algorithm, to detect and classify electronic parts. The model is trained on a robust dataset of electronic parts, which includes images of resistors, capacitors, diodes, and other essential components. This research addresses the challenges of automating the identification of components in electronic manufacturing and maintenance processes, where accuracy and speed are crucial. YOLOv9 and YOLOv11 models are employed to ensure efficient and precise part detection. The system is built using the Streamlit framework, making it accessible through an intuitive web interface, allowing users to upload images and receive component classifications. This project highlights the potential for computer vision to simplify tasks that traditionally require manual inspection. The results can improve operational efficiency and reduce errors in identifying electronic parts, contributing to a more automated and streamlined approach to electronics handling.

Keywords: Computer Vision, Automated Recognition, Electronic Parts, YOLOv9, YOLOv11, Image Classification, Deep Learning, Streamlit, Detection System, Web Interface.