Achelous: A Fast Unified Water-surface Panoptic Perception Framework
  based on Fusion of Monocular Camera and 4D mmWave Radar
Runwei Guan Shanliang Yao Xiaohui Zhu Ka Lok Man Eng Gee Lim Jeremy Smith Yong Yue Yutao Yue
Current perception models for different tasks usually exist in modular forms
on Unmanned Surface Vehicles (USVs), which infer extremely slowly in parallel
on edge devices, causing the asynchrony between perception results and USV
position, and leading to error decisions of autonomous navigation. Compared
with Unmanned Ground Vehicles (UGVs), the robust perception of USVs develops
relatively slowly. Moreover, most current multi-task perception models are huge
in parameters, slow in inference and not scalable. Oriented on this, we propose
Achelous, a low-cost and fast unified panoptic perception framework for
water-surface perception based on the fusion of a monocular camera and 4D
mmWave radar. Achelous can simultaneously perform five tasks, detection and
segmentation of visual targets, drivable-area segmentation, waterline
segmentation and radar point cloud segmentation. Besides, models in Achelous
family, with less than around 5 million parameters, achieve about 18 FPS on an
NVIDIA Jetson AGX Xavier, 11 FPS faster than HybridNets, and exceed YOLOX-Tiny
and Segformer-B0 on our collected dataset about 5 mAP$_{\text{50-95}}$ and 0.7
mIoU, especially under situations of adverse weather, dark environments and
camera failure. To our knowledge, Achelous is the first comprehensive panoptic
perception framework combining vision-level and point-cloud-level tasks for
water-surface perception. To promote the development of the intelligent
transportation community, we release our codes in
\url{https://github.com/GuanRunwei/Achelous}.