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Target detection is one of the most important contents in computer vision, which has been widely and effectively applied in production, daily life, and military. The target detection technology based on deep learning has gone far beyond the traditional target detection technology with high autonomy, accuracy and sensitivity. Based on convolutional neural network, it mainly develops two-stage detection algorithm and one-stage detection algorithm. The two-stage detection algorithms mainly include RCNN, SPP-Net, Fast-RCNN, Faster-RCNN, etc., and the one-stage detection algorithms mainly include Yolo, SSD, RetinaNet, etc. Deep learning framework is an important tool to implements target detection algorithm. Current mainstream deep learning frameworks include PyTorch, TensorFlow, PaddlePaddle, keras, etc.

The application of magnetic field detector in the actual road section mainly depends on the layout of these detectors in the actual traffic environment. For example, geomagnetism and coils are easily disturbed by vehicles near the road, resulting in missed or false detection; Video detection equipment depends on weather, visibility and other environmental conditions, and there will be a large degree of missed detection. Compared with the disadvantages of the above sensors, Millimeter-wave Radar has many unique advantages, including insensitivity to light or weather, wider application range compared with vision based technology, and higher accuracy,which makes millimeter-wave radar more outstanding in traffic monitoring applications. This paper presents a detection model of millimeter wave radar spectrum based on YOLOX, which is used to monitor traffic. Firstly, this paper enhances the millimeter-wave radar spectrum data used for training by gray-scale transformation, solves the problem that the Doppler target echo is not obvious, and obtains a clearer target image. Secondly, the latest YOLOX algorithm is used to train 2844 radar spectrum diagrams, and the performance evaluation is carried out to obtain the YOLOX average mAP@0.5 The value is 0.916 and FPS is 35.8. Finally, experiments show that YOLOX algorithm is better than YOLOv5 algorithm mAP@0.5 It is 2.9% higher, which proves the superiority of the algorithm.