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In recent years, unmanned surface vehicles (USVs) have been experiencing growth in various applications. With the expansion of USVs’ application scenes from the typical marine areas to inland waters, new challenges arise for the object detection task, which is an essential part of the perception system of USVs. In our work, we focus on a relatively unexplored task for USVs in inland waters: small object detection on water surfaces, which is of vital importance for safe autonomous navigation and USVs’ certain missions such as floating waste cleaning. Considering the limitations of vision-based object detection, we propose a novel radar-vision fusion based method for robust small object detection on water surfaces. By using a novel representation format of millimeter wave radar point clouds and applying a deep-level multi-scale fusion of RGB images and radar data, the proposed method can efficiently utilize the characteristics of radar data and improve the accuracy and robustness for small object detection on water surfaces. We test the method on the real-world floating bottle dataset that we collected and released. The result shows that, our method improves the average detection accuracy significantly compared to the vision-based methods and achieves state-of-the-art performance. Besides, the proposed method performs robustly when single sensor degrades.
Cheng et al. (Fri,) studied this question.