See Further Than CFAR
a Data-Driven Radar Detector Trained by Lidar
Ignacio Roldan Montero (TU Delft - Microwave Sensing, Signals & Systems)
Andras Palffy (TU Delft - Intelligent Vehicles, TU Delft - Microwave Sensing, Signals & Systems)
J.F.P. Kooij (TU Delft - Intelligent Vehicles)
D. M. Gavrila (TU Delft - Intelligent Vehicles)
Francesco Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
O. Yarovyi (TU Delft - Microwave Sensing, Signals & Systems)
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Abstract
In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with multiple objects that appear as extended targets. We propose a data-driven radar target detector exploiting a highly efficient 2D CNN backbone inspired by the computer vision domain. Our approach is distinguished by a unique cross-sensor supervision pipeline, enabling it to learn exclusively from unlabeled synchronized radar and lidar data, thuseliminating the need for costly manual object annotations. Using a novel large-scale, real-life multi-sensor dataset recorded in various driving scenarios, we demonstrate that the proposed detector generates dense, lidar-like point clouds, achieving a lower Chamfer distance to the reference lidar point clouds than CFAR detectors. Overall, it significantly outperforms CFAR baselines detection accuracy.