A Millimeter-Wave Radar-Aided Vision Detection Method for Water Surface Small Object Detection

Abstract

Unmanned surface vehicles (USVs) have wide applications in marine inspection and monitoring, terrain mapping, and water surface cleaning. Accurate and robust environment perception ability is essential for achieving autonomy in USVs. Small object detection on water surfaces is an important environment perception task, typically achieved by visual detection using cameras. However, existing vision-based small object detection methods suffer from performance degradation in complex water surface environments. Therefore, in this paper, we propose a millimeter-wave (mmWave) radar-aided vision detection method that enables automatic data association and fusion between mmWave radar point clouds and images. Through testing on real-world data, the proposed method demonstrates significant performance improvement over vision-based object detection methods without introducing more computational costs, making it suitable for real-time application on USVs. Furthermore, the image–radar data association model in the proposed method can serve as a plug-and-play module for other object detection methods.

GT-YOLO: Nearshore Infrared Ship Detection Based on Infrared Images

“…Machine vision, as a fusion technology that uses computers as tools and combines image processing with sensors, has emerged in multiple research directions such as object detection, semantic segmentation, motion tracking, 3D reconstruction, and action recognition. In many practical application scenarios, object detection has important application value, covering multiple fields such as video surveillance, autonomous driving, unmanned ship detection, and navigation [1]. The world has abundant ocean and inland river resources, so research on water target detection is increasingly receiving attention [2], especially in the field of ship detection at sea.…”