Radar Image Road Target Detection Method Based on Lightweight YOLOv5
Addressing the issue of unfiltered stationary clutter interference and the excessive number of network parameters and floating-point operations in the current vehicular millimeter-wave radar target de-tection signal processing,this paper proposes a signal processing chain for vehicular millimeter-wave ra-dar target detection.This methodology is underpinned by the integration of Ghost convolution and Spatial Depth(SPD)convolution,culminating in the design of a lightweight YOLOv5 target detection model tai-lored for vehicular radar applications.The process initiates with the suppression of stationary clutter inter-ference through the application of the mean subtraction algorithm on the raw AD sampled data.This is followed by the generation of the target's Range-Doppler(RD)image through a two-dimensional Fast Fourier Transform(FFT).Subsequently,the lightweight target detection network model is employed for the detection within the RD images.Empirical data corroborates the efficacy of the proposed approach in filtering out stationary clutter,significantly reducing the floating-point computation requirement of the tar-get detection model,thus minimizing the model's parameter count while simultaneously achieving high detection accuracy.
millimeter wave radartarget detectionrange-doppler imagelightweight network model
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