An Object Detection Approach for Automated Detection of Groove Line in Tube Yoke

Deep Neural Networks are designed explicitly for visionbased applications. They have become a standard method image classification, object detection, real-time processing capabilities, and other computer vision tasks. With the intervention of human beings, it is challenging businesses to verify that product appropriately created without any glitches, as this could result in errors due lack training, which further cause losses several complications. Thus, there need machine learning-based solution improves work efficiency accuracy identifying processed unprocessed parts. Machine Learning models advantages over tasks, including consistency, speed, scalability, costeffectiveness, improvement time. These can help organizations improve their operations achieve better outcomes. To do this, we propose an approach involves building model using detection method, You Only Look Once (YOLO), then deployed on Raspberry Pi integrated with assembly line carry out task validating parts by achieving 98% accuracy.