A Target Detection Algorithm Based on Fusing Radar with a Camera in the Presence of a Fluctuating Signal Intensity

Abstract

Radar point clouds will experience variations in density, which may cause incorrect alerts during clustering. In turn, it will diminish the precision of the decision-level fusion method. To address this problem, a target detection algorithm based on fusing radar with a camera in the presence of a fluctuating signal intensity is proposed in this paper. It introduces a snow ablation optimizer (SAO) for solving the optimal parameters of the density-based spatial clustering of applications with noise (DBSCAN). Subsequently, the enhanced DBSCAN clusters radar point clouds, and the valid clusters are fused with monocular camera targets. The experimental results indicate that the suggested fusion method can attain a Balance-score ranging from 0.97 to 0.99, performing outstandingly in preventing missed detections and false alarms. Additionally, the fluctuation range of the Balance-score is within 0.02, indicating the algorithm has an excellent robustness.