Channel Boosting Feature Ensemble for Radar-based Object Detection

TL;DR

This paper introduces a novel radar-based object detection method using a channel boosting feature ensemble and transformer network, significantly outperforming existing methods especially in adverse weather conditions.

Contribution

The paper proposes a new radar object detection approach combining channel boosting and transformer encoder-decoder, addressing limitations of camera and Lidar sensors in bad weather.

Findings

Outperforms state-of-the-art by over 12% in detection accuracy.

Effective in both good and adverse weather conditions.

Uses COCO metrics for comprehensive evaluation.

Abstract

Autonomous vehicles are conceived to provide safe and secure services by validating the safety standards as indicated by SOTIF-ISO/PAS-21448 (Safety of the intended functionality). Keeping in this context, the perception of the environment plays an instrumental role in conjunction with localization, planning and control modules. As a pivotal algorithm in the perception stack, object detection provides extensive insights into the autonomous vehicle's surroundings. Camera and Lidar are extensively utilized for object detection among different sensor modalities, but these exteroceptive sensors have limitations in resolution and adverse weather conditions. In this work, radar-based object detection is explored provides a counterpart sensor modality to be deployed and used in adverse weather conditions. The radar gives complex data; for this purpose, a channel boosting feature ensemble method with transformer encoder-decoder network is proposed. The object detection task using radar is formulated as a set prediction problem and evaluated on the publicly available dataset in both good and good-bad weather conditions. The proposed method's efficacy is extensively evaluated using the COCO evaluation metric, and the best-proposed model surpasses its state-of-the-art counterpart method by $12.55\%$ and $12.48\%$ in both good and good-bad weather conditions.