Fast Visual Identification and Location Algorithm forIndustrial Sorting Robots Based on Deep Learning

To overcome the problems of slow recognition, low accuracy and inaccurate positioning for industrial sorting robots, a fast visual identification and location algorithm based on deep convolutional neural network (CNN) is proposed. Firstly, the target image information is obtained by an industrial precision camera, and the target image is located and segmented through graying, filtering, Otsu binarization and boundary detection of the images. Secondly, the target object is identified by using a trained CNN, and its position coordinate and class are obtained. Thus, target sorting by industrial robots is realized. Finally, the Chinese chess with complex lines are taken in test experiments to verify the identification and location algorithm. Experimental results show that the locating error is lower than 0.8 mm, the fastest recognition speed can reach 0.049 seconds per target, and the identification accuracy can be kept over 98% in the experimental environment. So, the proposed algorithm has good accuracy and stability.