Lightweight Target Detection Network for Security Radar and FPGA Acceleration

Radar target detection technology has been widely used in many fields. In some application scenarios, such as security screening at a high-traffic airport, we need a flexible and real-time contraband detection system to ensure the safety of passengers. However, the object detection system based on deep learning usually has the problem of a large number of parameters, which will seriously affect its real-time performance if it is deployed on a small mobile platform. In this article, we will use model compression technology to lightweight the object detection model and deploy it into an embedded system for airport security screening. Firstly, we use a millimeter-wave radar device to collect datasets, and train a common single-stage target detection model SSD. Then we made some lightweight modifications to the model to reduce memory and computing power when deployed on an embedded mobile platform. Finally, we deploy the modified lightweight model combined with PaddleLite framework on the C5MB platform, and use the FPGA resources of the platform to accelerate inference. Experiments show that the detection speed of the proposed deployment method can reach 243.88ms/frame, which is 8.04 times faster than that directly deployed on the ARM platform, and the detection accuracy index mAP reaches 84.6%.