Hull First, Wake Second: Wake-Reliance Suppression for Robust Maritime Vessel Detection

2026-08-27

AI Summary

本文提出HullWake框架，通过优先检测船体再考虑尾流的方法解决海上船只检测中因依赖尾流导致的误检问题。

Abstract

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.

Problem

• maritime vessel detection
• wake-reliance problem
• false positives
• slow or stationary vessels
• water clutter

Innovation

• HullWake
• wake-reliance suppression
• bidirectional proposal-anchored corridors
• wake response supervision
• Curated-Wake