Detection method of various types of auto lamps based on improved Faster R-CNN model

Auto spray painting robots can realize the automation of various types of auto lamp spraying. The auto lamp detection algorithm is the key technology of the robot. The Faster R-CNN model is selected to detect the lamp, and some improvements are made to the model to achieve better detection effect. To generate more multi-dimensional feature expressions for auto lamps, residual network ResNet101 and feature pyra-mid network (FPN) are used to extract the features of auto lamps. Compatible Intersection over Union (CIoU) is used as the bounding box regression loss function to provide moving direction and more accurate position information for the bounding box and accelerate the convergence speed of the model. Through the auto lamp detection experiment, the average recognition accuracy of the improved model is 98.96%, and the detection speed is 0.46s per image. The improved model can realize the effective recognition of auto lamp types and meet the requirements of real-time processing.