Classification and Featurization of Motherboard Components Using YOLO Through Machine Learning

The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promising frontiers lies in the realm of Artificial Intelligence (AI), with machine learning at its forefront. Object detection is a crucial such part with applications in robotics and image analysis. This paper presents a comprehensive study on vision AI, focusing on the detection of motherboard components utilizing the YOLOv8 object detection framework. The results demonstrate the effectiveness and real time effectiveness of YOLOv8 in terms of motherboard object detection. Simultaneously, this work validates the feasibility of employing YOLOv8 for in-depth analysis and object detection on various circuit boards. This will allow many automated devices to repair circuit boards with the help of artificial intelligence. Consequently, allowing robots to detect flaws and repair integrated hardware components in situations considered difficult for humans.