Li Yiming, Zhang Bin, Liu Yichen, Wang Huibing, Zhang Shibo
Marine Engineering College, Dalian Maritime University, Dalian 116026, China.
Information Science and Technology College, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel). 2024 Sep 4;24(17):5756. doi: 10.3390/s24175756.
Detecting and tracking personnel onboard is an important measure to prevent ships from being invaded by outsiders and ensure ship security. Ships are characterized by more cabins, numerous equipment, and dense personnel, so there are problems such as unpredictable personnel trajectories, frequent occlusions, and many small targets, which lead to the poor performance of existing multi-target-tracking algorithms on shipboard surveillance videos. This study conducts research in the context of onboard surveillance and proposes a multi-object detection and tracking algorithm for anti-intrusion on ships. First, this study designs the BR-YOLO network to provide high-quality object-detection results for the tracking algorithm. The shallow layers of its backbone network use the BiFormer module to capture dependencies between distant objects and reduce information loss. Second, the improved C2f module is used in the deep layer of BR-YOLO to introduce the RepGhost structure to achieve model lightweighting through reparameterization. Then, the Part OSNet network is proposed, which uses different pooling branches to focus on multi-scale features, including part-level features, thereby obtaining strong Re-ID feature representations and providing richer appearance information for personnel tracking. Finally, by integrating the appearance information for association matching, the tracking trajectory is generated in Tracking-By-Detection mode and validated on the self-constructed shipboard surveillance dataset. The experimental results show that the algorithm in this paper is effective in shipboard surveillance. Compared with the present mainstream algorithms, the MOTA, HOTP, and IDF1 are enhanced by about 10 percentage points, the MOTP is enhanced by about 7 percentage points, and IDs are also significantly reduced, which is of great practical significance for the prevention of intrusion by ship personnel.
检测和跟踪船上人员是防止船舶被外部人员入侵并确保船舶安全的一项重要措施。船舶的特点是舱室较多、设备众多且人员密集，因此存在人员轨迹不可预测、遮挡频繁以及小目标众多等问题，这导致现有的多目标跟踪算法在船舶监控视频上的性能不佳。本研究在船舶监控的背景下进行研究，提出了一种用于船舶防入侵的多目标检测与跟踪算法。首先，本研究设计了BR-YOLO网络，为跟踪算法提供高质量的目标检测结果。其骨干网络的浅层使用BiFormer模块来捕获远距离物体之间的依赖关系并减少信息损失。其次，在BR-YOLO的深层使用改进的C2f模块，引入RepGhost结构以通过重新参数化实现模型轻量化。然后，提出了Part OSNet网络，该网络使用不同的池化分支来关注多尺度特征，包括部分级特征，从而获得强大的Re-ID特征表示并为人员跟踪提供更丰富的外观信息。最后，通过整合外观信息进行关联匹配，以检测跟踪模式生成跟踪轨迹，并在自建的船舶监控数据集上进行验证。实验结果表明，本文算法在船舶监控中是有效的。与当前主流算法相比，MOTA、HOTP和IDF1提高了约10个百分点，MOTP提高了约7个百分点，ID也显著减少，这对于防止船舶人员入侵具有重要的实际意义。
