Detection and Positioning of Grab Target Based on Deep Learning

A robot's high-accuracy attitude detection and location of the grab target is still an open problem. We propose a method based on the convolutional neural network, which uses the Faster R-CNN Inception-V2 network model, for high-accuracy and fast attitude detection and location of the grab target. In the network, the attitude angle of the grab target is determined using a classification label, and the position coordinates are obtained using a regression method. The Cornell public datasets are relabeled, and the end-to-end models are trained. The model achieves an accuracy of 96.18% and 96.32% on the instance and object detection test sets, respectively, and the processing time for each image is less than 0.06 s. The experimental results show that the model can achieve high-accuracy and fast attitude detection and location of single or multiple grab targets in animage in real time and has strong robustness and stability.