Research on automaticrecognition of industrial robot workpiece based on improved LeNet-5

In the production process of robot joint workpiece assembly, there are some problems, such as many kinds of workpieces, large output, time-consuming and laborious as manual sorting and assembly, etc. Based on the classic LeNet-5 model, this paper presents an improved LeNet-5 network, which can modify the size of inputting image to 32×32, increase the convolution layer to 4 layers, and use Leaky ReLU instead of the excitation function to prevent over-fitting. Meanwhile, the improved LeNet-5 is trained, tested and compared with classic LeNet-5 and GoogLeNet models. The experimental results show that the improved LeNet-5 has the best effect, the accuracy of the test set reaches 98.32%, the loss value of AUC is 0.916 3, and it only takes about 0.016 s to identify a workpiece to be assembled. It could meet the real-time detection requirements to industrial robots in factories, and provides an effective reference for similar identification, which has high application value.