Enhancing the dependability of autonomous surface vehicles through robustness benchmarking of real-time object detection models
Wang, Y.; Zhang, Z.; Wang, K.; Caesar, H.; Boydens, J.; Pissoort, D.; Verbeke, M. (2026). Enhancing the dependability of autonomous surface vehicles through robustness benchmarking of real-time object detection models. Exp. Syst. Appl. 296: 129151. https://dx.doi.org/10.1016/j.eswa.2025.129151
In: Expert Systems With Applications. Elsevier: New York; Oxford. ISSN 0957-4174; e-ISSN 1873-6793, more
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| Author keywords |
Corruption robustness;Real-time object detection;Autonomous surfaces vehicles;Artificial intelligence dependability;Robustness benchmark;maritime;navigation;tracking;ships |
| Authors | | Top |
- Wang, Y.
- Zhang, Z.
- Wang, K.
- Caesar, H.
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- Boydens, J.
- Pissoort, D., more
- Verbeke, M.
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| Abstract |
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. |
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