A Lightweight Model-Driven 4D Radar Framework for Pervasive Human Detection in Harsh Conditions

TL;DR

This paper introduces a lightweight, model-driven 4D radar framework for reliable human detection in harsh, visibility-degraded environments like underground tunnels and dusty industrial settings, operating efficiently on embedded hardware.

Contribution

It presents a novel, fully model-driven perception system utilizing 4D radar data for real-time human detection in challenging conditions, outperforming optical sensors in visibility-degraded environments.

Findings

Radar-based detector maintains stable pedestrian detection in dust and underground tunnels.

The framework operates in real-time on embedded hardware with high computational efficiency.

Robust perception is achieved without reliance on optical or LiDAR sensors.

Abstract

Pervasive sensing in industrial and underground environments is severely constrained by airborne dust, smoke, confined geometry, and metallic structures, which rapidly degrade optical and LiDAR based perception. Elevation resolved 4D mmWave radar offers strong resilience to such conditions, yet there remains a limited understanding of how to process its sparse and anisotropic point clouds for reliable human detection in enclosed, visibility degraded spaces. This paper presents a fully model-driven 4D radar perception framework designed for real-time execution on embedded edge hardware. The system uses radar as its sole perception modality and integrates domain aware multi threshold filtering, ego motion compensated temporal accumulation, KD tree Euclidean clustering with Doppler aware refinement, and a rule based 3D classifier. The framework is evaluated in a dust filled enclosed trailer and in real underground mining tunnels, and in the tested scenarios the radar based detector maintains stable pedestrian identification as camera and LiDAR modalities fail under severe visibility degradation. These results suggest that the proposed model-driven approach provides robust, interpretable, and computationally efficient perception for safety-critical applications in harsh industrial and subterranean environments.