Target Recognition of Industrial Robots Using Machine Vision in 5G Environment - Jin, Zhenkun; Liu, Lei; Gong, Dafeng; Li, Lei (Frontiers in Neurorobotics, 2021)
In the 5G environment, the errors of industrial robots in recognizing, detecting, and positioning objects are serious; in addition, the recognition speed and accuracy of industrial robots are unsatisfactory. Therefore, in order to solve these problems, the image layers are convolved and pooled through the deep learning model of artificial intelligence, and the visual recognition system of industrial robots are optimized through the advanced methods of the target classification algorithm. Then, the algorithm of the improved Fast R-CNN target detection model is verified by the bottled objects. The VGG-16 classification network based on Hyper-Column scheme is verified by targeting the bottled objects in small and complex environments. The results have shown that both the Fast R-CNN algorithm and the improved VGG-16 classification network based on the Hyper-Column scheme can complete the localization and recognition of the target objects, indicating that the Fast R-CNN algorithm and the Hyper-Column-based scheme have improved the accuracy and effectiveness of target recognition and positioning of VGG-16 classification network.