Deep Modelling of Millimetre Wave Radar Obstacle Detection for Smart Driving

A depth detection model is designed based on millimetre wave radar for smart driving obstacle sensing requirements. The target features are extracted by improving the ResNet50 network structure, and the target detection is achieved by optimising the Faster R-CNN. The experimental data show that the model detection accuracy reaches 93.5%, the processing delay is 15.3ms, the target detection rate is 98.3% in the range of 50m, and the average error of speed estimation is 0.5m/s. The model maintains 92.3% detection accuracy in rainy days and other complex environments, which is of practical application value for improving the performance of automatic driving perception system.