The Autonomous Surface Vehicle (ASV) market is expected to double by 2030, rapidly transforming maritime logistics through faster deliveries, lower costs, reduced risks from human error, and the potential to save human lives. ASVs depend on robust object detection models to ensure safe navigation. However, existing models are often susceptible to natural corruptions such as blur, noise, adverse weather, and occlusions-risks to perception robustness further intensified by the lack of domain-specific robustness benchmarks. To fill this gap, we propose the first waterborne-focused robustness benchmark, incorporating 25 synthetic corruptions (15 adapted from ImageNet-C plus 10 novel ones for ASVs) across five severity levels. We also incorporate mixed corruptions to capture real-world complexity. Building on three public waterborne datasets (SeaShips, SMD, SSAVE), we create SeaShips-C, SMD-C, and SSAVE-C, each augmented with our corruption suite. A comprehensive robustness evaluation is conducted on multiple sizes of YOLOv8, SSD, NanoDet-Plus, and RT-DETR, revealing critical vulnerabilities: e.g., YOLOv8n's mAP⁵⁰ drops by 43.0 % under contrast corruption on SeaShips-C, reaching a 59.5 % decline when combined with raindrops. Larger variants (e.g., YOLOv8x) exhibit greater robustness, offering insights for safer deployments. Aligned with ISO/IEC TR 5469 and IEC 61508, our benchmark supports pre-deployment verification. By identifying risk-prone conditions, practitioners can apply targeted mitigation strategies, such as data augmentation and human oversight. To promote further research and support industrial practice, we provide open access to all benchmark datasets and code-which can also serve as a data augmentation resource to enhance model training.

Autonomous surface vessels (ASVs) increasingly gain appeal in the maritime industry for their high efficiency and improved navigational capabilities. However, risks originating from various internal and external factors such as faults, traffic, harsh weather conditions, etc., can affect their guidance and control capabilities and impact nominal vessel operations. The existing risk mitigation methods mainly focus on the vessel's guidance system and do not consider unsafe actions due to the control system. In this paper, we propose a new method based on a partially observable Markov decision process (POMDP) model for the online risk mitigation of autonomous inland vessels. The POMDP model-based method utilizes information about situational awareness to assist the vessel's planning and control system in real-time decision-making during hazardous situations, thereby ensuring that the vessel remains in a minimum-risk condition. Based on the identified risk-influencing factors (RIFs), the transition probabilities are updated by a Bayesian belief network (BBN). A case study of an autonomous inland vessel navigating in a confined waterway is presented to demonstrate the capability of the proposed method.