Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework
  on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous
  Modalities
Runwei Guan Haocheng Zhao Shanliang Yao Ka Lok Man Xiaohui Zhu Limin Yu Yong Yue Jeremy Smith Eng Gee Lim Weiping Ding Yutao Yue
Urban water-surface robust perception serves as the foundation for
intelligent monitoring of aquatic environments and the autonomous navigation
and operation of unmanned vessels, especially in the context of waterway
safety. It is worth noting that current multi-sensor fusion and multi-task
learning models consume substantial power and heavily rely on high-power GPUs
for inference. This contributes to increased carbon emissions, a concern that
runs counter to the prevailing emphasis on environmental preservation and the
pursuit of sustainable, low-carbon urban environments. In light of these
concerns, this paper concentrates on low-power, lightweight, multi-task
panoptic perception through the fusion of visual and 4D radar data, which is
seen as a promising low-cost perception method. We propose a framework named
Achelous++ that facilitates the development and comprehensive evaluation of
multi-task water-surface panoptic perception models. Achelous++ can
simultaneously execute five perception tasks with high speed and low power
consumption, including object detection, object semantic segmentation,
drivable-area segmentation, waterline segmentation, and radar point cloud
semantic segmentation. Furthermore, to meet the demand for developers to
customize models for real-time inference on low-performance devices, a novel
multi-modal pruning strategy known as Heterogeneous-Aware SynFlow (HA-SynFlow)
is proposed. Besides, Achelous++ also supports random pruning at initialization
with different layer-wise sparsity, such as Uniform and Erdos-Renyi-Kernel
(ERK). Overall, our Achelous++ framework achieves state-of-the-art performance
on the WaterScenes benchmark, excelling in both accuracy and power efficiency
compared to other single-task and multi-task models. We release and maintain
the code at https://github.com/GuanRunwei/Achelous.