Abstract:At present, the traditional industrial robot visual sorting technology has been unable to effectively detect and identify workpiece with complicated shapes and dense placement. Therefore, in order to improve the accuracy of sorting workpiece detection on the production line, a target detection algorithm based on Cuckoo Search (CS) optimized deep learning Convolutional Neural Network (CNN) is proposed. The composition of the visual sorting system was first analyzed. Then the model structure of the classic Faster R-CNN is used to achieve the target detection, and the CS optimization algorithm is applied to the parameter training of the CNN model, which solves the local optimal problem of back propagation and improves the iteration speed. The experimental results of workpiece inspection show that compared with the traditional CNN model, the proposed CS-CNN model has better accuracy of target detection and improves the convergence speed of the network.