Improving the Performance of RODNet for MMW Radar Target Detection in Dense Pedestrian Scene

In the field of autonomous driving, millimeter-wave (MMW) radar is often used as a supplement sensor of other types of sensors, such as optics, in severe weather conditions to provide target-detection services for autonomous driving. RODNet (A Real-Time Radar Object-Detection Network) is one of the most widely used MMW radar range–azimuth (RA) image sequence target-detection algorithms based on Convolutional Neural Networks (CNNs). However, RODNet adopts an object-location similarity (OLS) detection method that is independent of the number of targets to obtain the final target detections from the predicted confidence map. Therefore, it gives a poor performance on missed detection ratio in dense pedestrian scenes. Based on the analysis of the predicted confidence map distribution characteristics, we propose a new generative model-based target-location detection algorithm to improve the performance of RODNet in dense pedestrian scenes. The confidence value and space distribution predicted by RODNet are analyzed in this paper. It shows that the space distribution is more robust than the value distribution for clustering. This is useful in selecting a clustering method to estimate the clustering centers of multiple targets in close range under the effects of distributed target and radar measurement variance and multipath scattering. Another key idea of this algorithm is the derivation of a Gaussian Mixture Model with target number (GMM-TN) for generating the likelihood probability distributions of different target number assumptions. Furthermore, a minimum Kullback–Leibler (KL) divergence target number estimation scheme is proposed combined with K-means clustering and a GMM-TN model. Through the CRUW dataset, the target-detection experiment on a dense pedestrian scene is carried out, and the confidence distribution under typical hidden variable conditions is analyzed. The effectiveness of the improved algorithm is verified: the Average Precision (AP) is improved by 29% and the Average Recall (AR) is improved by 36%.