Pipeline Multitype Artifact Recognition Method Based on Inception_Resnet _V2 Structure Improving SSD Network

Abstract

A fast recognition method for assembly line workpieces based on an improved SSD model is proposed to address the problems of low detection accuracy and lack of real-time performance when existing target detection models face small-scale targets and stacked targets. Based on the SSD network, the optimized Inception_Resnet _V2 structure is used to improve its feature extraction layer and enhance the extraction capability of the network for small-scale targets. The repulsion loss (Reploss) is used to optimize the loss function of the SSD network to solve the problem of stacked workpieces. The issue of difficult detection is improved. The robustness of the algorithm is enhanced. The experimental results show that the improved SSD target detection method improves the detection accuracy by 9.69% over the traditional SSD map. The detection speed meets the real-time requirements, which is a better balance of detection real time and accuracy requirements. The algorithm can recognize small-scale and stacked targets with higher category confidence, better algorithm robustness, and better recognition performance compared to the same type of target detection algorithms.