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This paper introduces HullWake, a novel framework designed to enhance maritime vessel detection by prioritizing hull features over wake cues, addressing the wake-reliance issue that leads to missed detections and false positives. By employing a combination of hull-first evidence extraction and wake response supervision, HullWake effectively separates and suppresses wake-dominant predictions, leading to improved detection performance in challenging scenarios. Experiments on the newly curated Curated-Wake dataset demonstrate significant advancements in average precision (AP), robustness against weak or absent wakes, and overall confidence stability compared to existing detection methods.
HullWake achieves a remarkable reduction in false positives and improved detection rates for slow or stationary vessels, redefining robustness in maritime detection systems.
Maritime vessel detectors often face scenes where hulls are small, low-contrast, or blurred, while wakes are longer and easier to detect. This creates a wake-reliance problem: detectors may miss slow or stationary vessels with weak wakes, or produce false positives on wake-like water clutter. We propose HullWake, a hull-first wake-second framework for robust maritime vessel detection. HullWake separates proposal-centered hull evidence from directional wake context, extracts wake cues with bidirectional proposal-anchored corridors, and suppresses wake-dominant predictions through wake response supervision, wake-attenuated consistency, wake-only confidence suppression, and hull--wake decorrelation. We also introduce a wake-oriented evaluation protocol covering weak/no-wake vessels, wake-like hard negatives, worst-group AP, and confidence drop after wake attenuation. Experiments are conducted on Curated-Wake, a wake-oriented maritime dataset of about 10,000 images curated from Ships/Vessels in Aerial Images, the SMD benchmark, and SeaDronesSee, with newly added detection- and segmentation-level wake annotations. Compared with box-only detectors and mask-supervised segmentation baselines, HullWake improves overall AP, weak/no-wake robustness, wake-like false positives, worst-group AP, and confidence stability after wake attenuation.
