Target detection is the basic technology of automatic driving system. Deep learning has gradually become the mainstream target detection algorithm because of its powerful feature extraction ability and adaptive ability. How to ensure accuracy and speed is a great challenge in the field of target detection. In order to solve the problems of high miss detection rate of small target and difficult to realize embedded real-time detection in the process of complex environment detection by deep learning method, this paper adds two auxiliary remaining network blocks in the backbone network. So that the backbone network can extract the global and local features of the detected object, and carry out feature extraction based on the feature pyramid network Fusion, adding a scale to form a three scale prediction, to improve the problem of poor detection accuracy of yolov4-tiny network. The simulation results show that: Compared with yolov4-tiny, the accuracy of the improved network structure is improved by 3.3%, and the detection speed is 251 fps, which ensures the requirements of real-time detection. This algorithm has good detection effect in the case of lack of illumination and target occlusion, and its detection accuracy on the mixed data set is better than that of the contrast algorithm, which meets the real-time detection conditions and is suitable for deployment on the embedded system carried by the car.

Radar non-contact monitoring of vital signs has a broad application prospect in clinical monitoring. Aiming at the problem of strong interference in non-contact vital signs detection (Such as multi-target, random body motion), a blind source separation (BSS) signal detection method based on Fast-ICA is proposed to reduce the interference of multi-target. In this algorithm, entropy is used to evaluate the non Gaussian property, and the appropriate transformation matrix is selected, according to the statistical independence of the signals, the source signals are separated from the observed mixed signals. On this basis, the traditional blind source separation process is improved, and the wavelet transform preprocessing algorithm based on translation invariant is added to suppress the interference of static clutter. The feasibility of this method is verified by simulation experiments.