Few-Shot Object Detection Algorithm Based on Geometric Prior and Attention RPN

Intelligent factories driven by deep vision technology use robotic arms to perform tasks such as picking and assembling in a production environment. In practical applications, with the continuous changes of products on the industrial pipeline, the detection model needs to continuously train new weights to adapt to new application scenarios. It is time-consuming and labor-intensive to manually collect training data when deploying the production line, and it cannot be quickly adapted in industrial scenarios. Therefore, we propose an attention RPN (Region Proposal Network) few-shot object detection algorithm based on geometric prior. The algorithm uses the attention RPN module to strengthen the feature extraction ability of the detection model and uses the virtual simulation software to generate synthetic data similar to the real object geometry as the base class data to train the feature extraction network so that the network obtains the ability to extract geometric features on the base class object. By comparing the learning strategies, only a small number of real data samples are used to train the detection model twice. The experimental results show that the algorithm can detect more objects than the existing few-shot object detection algorithm in the industrial scene with only a small amount of real sample data, and the detection accuracy can reach 97%.