HMPNet: A Feature Aggregation Architecture for Maritime Object Detection from a Shipborne Perspective

• URL: http://arxiv.org/abs/2505.08231v1
• Date: Tue, 13 May 2025 05:17:53 GMT
• Title: HMPNet: A Feature Aggregation Architecture for Maritime Object Detection from a Shipborne Perspective
• Authors: Yu Zhang, Fengyuan Liu, Juan Lyu, Yi Wei, Changdong Yu,
• Abstract summary: A novel dataset annotated for 12 object categories under diverse maritime environments and weather conditions is presented.<n>We propose HMPNet, a lightweight architecture tailored for shipborne object detection.<n> Empirical evaluations indicate that HMPNet surpasses current state-of-the-art methods in terms of both accuracy and computational efficiency.
• Score: 16.421691711725916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of intelligent maritime navigation, object detection from a shipborne perspective is paramount. Despite the criticality, the paucity of maritime-specific data impedes the deployment of sophisticated visual perception techniques, akin to those utilized in autonomous vehicular systems, within the maritime context. To bridge this gap, we introduce Navigation12, a novel dataset annotated for 12 object categories under diverse maritime environments and weather conditions. Based upon this dataset, we propose HMPNet, a lightweight architecture tailored for shipborne object detection. HMPNet incorporates a hierarchical dynamic modulation backbone to bolster feature aggregation and expression, complemented by a matrix cascading poly-scale neck and a polymerization weight sharing detector, facilitating efficient multi-scale feature aggregation. Empirical evaluations indicate that HMPNet surpasses current state-of-the-art methods in terms of both accuracy and computational efficiency, realizing a 3.3% improvement in mean Average Precision over YOLOv11n, the prevailing model, and reducing parameters by 23%.

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