Revolutionizing Car Assembly Line Efficiency Using Multi-Object Detection and Tracking

This paper presents a novel approach to address the challenges of manual labor reliance and inefficiencies in the automotive manufacturing industry through the implementation of computer vision-based object detection and tracking. By leveraging advanced technologies, such as AI-powered object tracking, the proposed system aims to streamline production processes, enhance quality control, and reduce the need for manual intervention. This study explores the integration of cutting-edge technologies, such as BYTETrack, YOLOv8, and Roboflow Supervision, to revolutionize multi-object detection and tracking in automotive production lines. Through a comparative analysis of these technologies, this study highlights their unique features and capabilities, highlighting their potential to optimize efficiency and productivity in the manufacturing sector. Overall, this research contributes to ongoing efforts to enhance automation and drive progress in the automotive manufacturing industry.