Research on the target cargo identification method based on CNN
Aiguo Li, Ruifang Gao, Jiahao Fu, Fan Yang · 2021
The automatic inspection robot can effectively pre-vent cargo from being stolen, replaced and opened without authorization. However, it will be disturbed by external factors such as occlusion, illumination and angle when it takes photos to identify cargo, which will affect the recognition effect. In order to improve the recognition effect of the automatic inspection robot on the target goods, this paper first detects the object in the data preprocessing stage, extracts the goods con-tour, and then carries on the image recognition, which im-proves the recognition rate. Then we compare the loss rate and accuracy rate of four network models: AlexNet, GoogleNet, ResNet and MobileNet. The experimental results show that ResN et algorithm has the highest image recognition accuracy, is more friendly to the recognition of light and occlusion, and is more in line with the actual operation needs of automatic inspection robot. It has reference value for the research of target cargo recognition.