Radar Object Detection Using Data Merging, Enhancement and Fusion

Jun Yu, Xinlong Hao, Xinjian Gao, Qiang Sun, Yuyu Liu, Peng Chang, Zhong Zhang, Fang Gao, Feng Shuang

Abstract: Compared to visible images, radar images are generally considered to be an active and robust solution, even in adverse driving situations, for object detection. However, the accuracy of radar object detection (ROD) is always poor. Owing to taking full advantage of data merging, enhancement and fusion, this paper proposes an effective ROD system with only radar images as the input. First, an aggregation module is designed to merge the data from all chirps in the same frame. Then, various gaussian noises with different parameters are employed to increase data diversity and reduce over-fitting based on the analysis of training data. Moreover, due to the process of inference with default parameters is not accurate enough, some hyperparameters are changed to increase the accuracy performance. Finally, a combination strategy is adopted to benefit from multi-model fusion. ROD2021 Challenge is supported by ACM ICMR 2021, and our team (ustc-nelslip) ranked 2nd in the test stage of this challenge. Diverse evaluations also verify the superiority of the proposed system.