WADNet: Weather Adversarial Decoupling Network for Marine Ship Instance Segmentation

Abstract

Robust visual instance segmentation for autonomous maritime surface vehicles remains challenging under adverse weather conditions such as fog and rain, which can cause contrast attenuation, boundary blurring, and visual interference. To address these challenges, we propose WADNet, a weather-adaptive instance segmentation framework for marine ship instance segmentation. At the macro level, a Weather-Adversarial Feature Decoupling (WAFD) mechanism employs gradient-reversal-based adversarial learning during training to encourage the backbone representation to become less dependent on weather-related visual cues, while introducing no additional inference-time computation. At the feature-processing level, a Weather-Adaptive Dynamic Routing Mechanism (WADRM) derives input-dependent routing weights from global feature statistics and softly combines multiple convolutional experts to provide condition-dependent feature transformation under varying weather conditions. Furthermore, Inner-CIoU is incorporated into the bounding-box regression objective to reduce the influence of uncertain peripheral regions during localization. Experiments on the evaluated mixed-weather maritime dataset demonstrate improved instance segmentation performance under the tested conditions. Specifically, under single-pass inference without test-time augmentation, WADNet achieves a Mask AP of 63.40%, outperforming the YOLO11n-seg baseline by 3.18 percentage points while maintaining high-throughput inference on the evaluated GPU platform.