针对船载场景的海面目标检测，提出轻量级模型HMPNet
HMPNet: A Feature Aggregation Architecture for Maritime Object Detection from a Shipborne Perspective
- 设计分层动态调制主干网络增强特征聚合
- 在Navigation12数据集上实现3.3% mAP提升，参数减少23%
- 适合资源受限的船舶智能导航系统部署
在智能航海领域，船载视角下的目标检测至关重要。然而，缺乏专门的海上数据阻碍了复杂视觉感知技术在该领域的应用，类似于自动驾驶中的技术。为填补这一空白，我们构建了Navigation12数据集，涵盖12类目标，在多种海上环境和天气条件下进行标注。基于此，提出HMPNet，一种专为船载目标检测设计的轻量级架构。HMPNet采用分层动态调制主干网络以强化特征聚合与表达，结合矩阵级联多尺度颈部和聚合权值共享检测头，实现高效多尺度特征融合。实证表明，HMPNet在准确率与计算效率方面均优于现有最优方法：相较于主流模型YOLOv11n，mAP提升3.3%，参数量减少23%。
原文摘要 · Abstract (English)
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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