## 1 Introduction  
As important physical indicators of life activities [1], the heartbeat frequency and respiratory rate of a person have collection requirements when it comes to medical diagnosis, biological research, and other fields. With the increasing development of medical technology and biological science, the demand for noncontact detection of one's heart and respiratory rate has gradually increased. Many accurate algorithms that perform frequency modulated continuous wave (FMCW) system radar signal detection exist. [PERSON] et al. [2] further developed the polarization detection optimization filter (PDOF) based on [PERSON]'s inequality and matrix decomposition theory; namely, they developed the subspace form of the PDOF, which enhances the detection performance. [PERSON] et al. [3] expressed the LDA manifold projection (the cost function maximizes the interclass distance and minimizes the intraclass distance) as an optimization problem in the Stiefel manifold. Four robust LDA-MIG detectors that correspond to different geometric measurements were proposed. [PERSON] [4] optimized the FMCW signal processing algorithm in an automotive radar system by eliminating the ambiguity between the frequency difference and Doppler frequency produced by the distance. In the past three decades, the FMCW radar has also been used for ocean surface velocity measurements [5] and snow detection [6]. Additionally, due to its large bandwidth and high resolution, the FM continuous wave radar is gradually applied to the life signal detection field [7]. [PERSON] et al. [8] proposed a multitarget detection and recognition algorithm based on the energy spectrum, which solved the problem of single-base life signal detection radars easily missing when detecting and recognizing multiple human targets, but they did not mention the extraction and recognition of heartbeat signals. [PERSON] et al. [9] proposed a heartbeat signal separation technology based on filtering with a 35 GHz radar, but this technology is not applicable in general environments because ECG information cannot be obtained. After [PERSON] et al. [10] first proposed the use of an FMCW millimeter wave radar to detect vital signs, [PERSON] et al. [11] used the phase information of a 77 GHz FMCW millimeter wave radar intermediate frequency signal to detect human vital signs; by doing so, they avoided the influence of high-order harmonics and also solved the problem of the DC signal frequency shift caused by the I/Q two-way signal, which does not consider the interference between the harmonics of the respiratory and life signal or the existence of environmental noise. [PERSON] et al. [12] proposed a noise elimination method and used an IIR bandpass filter to separate respiration and heartbeat signals to avoid interference between heartbeat and respiration signal harmonics. In previous studies, the detection was performed in a simple environment, and the target range gate was mostly judged within the first three peaks of the range-dimension FFT by default, or the maximum average value of the different distance intervals was calculated on each frame signal by considering the sweeping frequency power. However, in a complex environment, due to the existence of invalid targets or multiple living bodies, multiple high peak points will be generated during a distance-dimension FFT, interference will be generated during phase signal separation, and the phase information of the target cannot be separated via directivity. The application of the Yolo neural network in image recognition and practical engineering problems is becoming increasingly extensive. [PERSON] et al. [13] used their own enhanced Yolo neural network to identify pavement cracks. [PERSON] et al. [14] also used the improved R-CNN (regional convolutional neural networks) neural network to detect asphalt pavement cracks. [PERSON] et al. [15] developed an enhanced and accurate automated detection system for the Yolo v5 model.