Estimating Obstacle Maps for USVs Based on a Multistage Feature Aggregation and Semantic Feature Separation Network

Abstract

Obstacle map estimation based on efficient semantic segmentation networks is promising for improving the environmental awareness of unmanned surface vehicles (USVs). However, existing networks perform poorly in challenging scenes with small obstacles, scenery reflections, boat wakes, and visual ambiguities caused by unfavorable weather conditions. In this paper, we address the small obstacle segmentation problem by learning representations of obstacles at multiple scales. An efficient multistage feature aggregation (MFA) module is proposed, which utilizes fully separable convolutions of different sizes to capture and fuse multiscale context information from different stages of a backbone network. In addition, a novel feature separation (FS) loss function based on Gaussian mixture model is presented, which encourages the MFA module to enforce separation among different semantic features, thereby providing a robust and discriminative representation in various challenging scenes. Building upon the MFA module and the FS loss function, we present a fast multistage feature aggregation and semantic feature separation network (FASNet) for obstacle map estimation of USVs. An extensive evaluation was conducted on a challenging public dataset (MaSTr1325). We validated that various lightweight semantic segmentation models achieved consistent performance improvement when our MFA module and FS loss function were adopted. The evaluation results showed that the proposed FASNet outperformed state-of-the-art lightweight models and achieved 96.71% mIoU and > 1.5% higher obstacle-class IoU than the second-best network, while running over 58 fps.