Surround-view Water Surface BEV Segmentation for Autonomous Surface Vehicles: Dataset, Baseline and Hybrid-BEV Network

Autonomous surface vessels (ASVs) are growing rapidly due to their ability to execute hazardous and time-consuming missions over water surfaces. Recently, camera-based surround-view bird's eye view (BEV) segmentation has attracted increasing attention because of its popular application in autonomous driving. However, most existing surround-view BEV segmentation studies have focused on road scenes for autonomous cars, and the development of water surface surround-view BEV segmentation has been relatively slow due to the absence of water surface BEV datasets. Different from road scenes with complex dynamic scenes, water surface BEV segmentation task also face new interference like vessel swaying and reflection interference. To address these problems and stimulate relevant research, we first introduce the WSBEV dataset, a surround-view visual segmentation dataset on water surface scenes. Notably, our compiled WSBEV utilizes a hybrid camera configuration and contains pinhole cameras and fisheye cameras with different focal lengths, which also brings new challenges for the surround-view BEV segmentation method. To address the issues above, we propose a novel water surface BEV segmentation network named Hybrid-BEV that supports hybrid camera inputs. The geometry-based view transformer module is designed to directly transfer the extracted feature from the perspective view to BEV space. The pose query adjustment module introduces vessel pose data to suppress vessel swaying interference. Compared to other baseline methods, our Hybrid-BEV achieves excellent accuracy in our WSBEV dataset. Extensive experiments validate the effectiveness of our approach and the exceptional generalizability of the WSBEV dataset.