All-in-one Defensive Network (ADNet): Trustworthy Segmentation of Complex Maritime Environments for Unmanned Surface Vessels (USVs)

Abstract

The visual perception system of unmanned surface vessels (USVs) is often subjected to various adversarial attacks (e.g., lens stains, sun glare, ship painting, etc.), impacting the safety of autonomous navigation in maritime environments. To enhance the reliability and robustness of situational awareness in complex environments, we proposed a defensive model to effectively counteract multiple attacks targeting the perception system. Specifically, we first constructed a maritime instance segmentation dataset including various adversarial attack samples, with accurate annotations for the sky, water, land, ships and obstacles. To address the degradation in perception accuracy caused by adversarial attacks, we introduced a Monte Carlo-based random fusion module (MC Fusion) to enhance the adaptability of USVs in various dynamic environments. Additionally, as USVs are always equipped with onboard PC with limited computing resources, we incorporated the lightweight universal inverted bottleneck (UIB) module into the backbone to ensure effective feature extraction while reducing model parameters. Finally, we conducted comparative experiments under various adversarial attack scenarios. Our results demonstrate that, even in the presence of multiple adversarial attacks, our method improves ship detection accuracy by 13.9% and increases the mean accuracy of segmentation masks by over 10% compared to state-of-the-art models, enhancing the safety of USVs in navigation. The source code and datasets are available at https://github.com/huangyanh/ADNet.