Radar Timing Range–Doppler Spectral Target Detection Based on Attention ConvLSTM in Traffic Scenes

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 17; p. 4150
• Main Authors: Jia, Fengde, Tan, Jihong, Lu, Xiaochen, Qian, Junhui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Algorithms; Artificial intelligence; Cameras; Classification; Comparative analysis; ConvLSTM; Data processing; Datasets; Deep learning; Doppler radar; False alarms; Fourier transforms; Harsh environments; Hypothesis testing; Lidar; Machine learning; Mass production; Methods; Millimeter waves; millimeter-wave radar; Neural networks; object detection; Object recognition; Radar; Radar detection; Radar range; radar range–Doppler spectrum; Radar systems; Remote sensing; Semantics; Sensors; Target detection; Time series; Traffic; Vehicles; Velocity; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15174150

With the development of autonomous driving and the emergence of various intelligent traffic scenarios, object detection technology based on deep learning is more and more widely applied to real traffic scenarios. Commonly used detection devices include LiDAR and cameras. Since the implementation of traffic scene target detection technology requires mass production, the advantages of millimeter-wave radar have emerged, such as low cost and no interference from the external environment. The performance of LiDAR and cameras is greatly reduced due to their sensitivity to light, which affects target detection at night and in bad weather. However, millimeter-wave radar can overcome the influence of these harsh environments and has a great auxiliary effect on safe driving on the road. In this work, we propose a deep-learning-based object detection method considering the radar range–Doppler spectrum in traffic scenarios. The algorithm uses YOLOv8 as the basic architecture, makes full use of the time series characteristics of range–Doppler spectrum data in traffic scenarios, introduces the ConvLSTM network, and exerts the ability to process time series data. In order to improve the model’s ability to detect small objects, an efficient and lightweight Efficient Channel Attention (ECA) module is introduced. Through extensive experiments, our model shows better performance on two publicly available radar datasets, CARRADA and RADDet, compared to other state-of-the-art methods. Compared with other mainstream methods that can only achieve 30–60% mAP performance when the IOU is 0.3, our model can achieve 74.51% and 75.62% on the RADDet and CARRADA datasets, respectively, and has better robustness and generalization ability.