Design and Implementation of An Intelligent Automated System for Industrial Lamp Cup Inspection and Grading based on Machine Vision

In the manufacturing process of lamp cups, the quality of spot inspection directly affects the lighting effect of the lamp cups. Traditional manual inspection methods are inefficient and susceptible to subjective factors. This paper proposes an intelligent lamp spot inspection and grading system based on You Only Look Once version 5 (YOLOv5). The proposed system integrates Industrial Internet of Things (IIoT) technology and leverages the efficient target detection capabilities of the YOLOv5 algorithm to construct feature models, achieving intelligent automated detection of spot quality and automated grading of lamp cups. Compared with traditional manual detection methods, the designed system effectively improves production efficiency, reduces manual intervention, and provides technical support for the intelligent and automated production of the lamp manufacturing industry. Test experiments are conducted in actual production lines of lamp cups and have verified the effectiveness and real-time performance of the designed system.