Multi-type Auto Lamp Detection Method Based on Improved Faster R-CNN
Autonomous spray painting robots can realize the automatic spraying of various types of automobile lamps,and the lamp detection algorithm based on machine vision is the key technology of the robot.In view of the lack of deep learning algorithm to detect auto lamps in spraying environment,a detection algorithm based on improved Faster R-CNN is proposed.In the improved al-gorithm,the dense residual network(De-ResNet)is used to replace the original feature extraction network,which integrates multi-level feature information,increases the network depth,and avoids the disappearance of network gradient.At the same time,Distance-IoU(DIoU)is used to improve the loss function in the original algorithm.The improved IOU introduces a good distance measurement to further improve the detection accuracy.The experimental results show that the average accuracy of the improved al-gorithm is 98.56%,and the average recognition time of a single image is 0.45 s.It can realize the effective recognition of lamp types and meet the requirements of real-time processing.
spray painting robotobject detectionFaster R-CNNDenseNetloss function
国家科技期刊平台
NSTL
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