A warning framework for avoiding vessel‐bridge and vessel‐vessel collisions based on generative adversarial and dual‐task networks
A warning framework for avoiding vessel‐bridge and vessel‐vessel collisions based on generative adversarial and dual‐task networks
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DOI：
10.1111/mice.12757
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发表时间：
2021-09
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影响因子：
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通讯作者：
Bo Zhang-;Zhaofeng Xu;Jian Zhang;Gang Wu
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文献类型：
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作者：
Bo Zhang-;Zhaofeng Xu;Jian Zhang;Gang Wu
在河上很可能发生船桥碰撞和船船碰撞。为了避免这两种碰撞，本文提出了一种基于视频的船舶预警框架，该框架主要包括船舶定位、船舶轨迹数据增强和轨迹异常检测与预测。首先，提出了一种基于单应性的船舶实时定位方法。该方法以浮标为控制点，利用航拍图像获取其瞬时世界坐标，求解单应性问题。根据获得的单应性，将识别出的船舶中心像素坐标映射到相应的世界坐标，实现船舶定位。其次，提出了具有多重批评的轨迹生成对抗网络（tans - MC），以丰富历史轨迹，特别是异常轨迹。tans‐MC包含一个生成器和多个批评家。该生成器基于循环神经网络（RNN），用于生成可变长度的轨迹序列。批评家使用一维卷积和一维自适应池来获取轨迹特征。在tans - MC中使用不同结构的多个批评家来引导生成器生成不同的轨迹。第三，提出了一种双任务网络，用于发现具有异常轨迹的预警船-桥碰撞船舶，并预测预警船-船碰撞船舶的轨迹。双任务网络采用基于RNN的编码器-解码器结构，并具有两个分支，从而可以共同执行双任务。异常检测分支采用监督二值分类，输出风险程度。预测分支采用注意机制。最后，开发了一种实时碰撞预警系统，并在某桥梁上进行了应用。
The vessel‐bridge and vessel‐vessel collisions are likely to occur on the river. For avoiding the two kinds of collisions, a video‐based framework about early warning is proposed in this paper, which mainly contains vessel positioning, vessel trajectory data augmentation, and trajectory anomaly detection and prediction. At first, a real‐time vessel positioning method is proposed based on homography. In this method, the buoys are used as the control points, whose instantaneous world coordinates are obtained based on aerial photography, for solving the homography. Based on the obtained homography, the pixel coordinates of the identified vessel center can be mapped to the corresponding world coordinates, which realizes the vessel positioning. Second, the trajectory generative adversarial networks with multiple critics (TGANs‐MC) is proposed to enrich the historical trajectories, especially the abnormal trajectories. TGANs‐MC contains a generator and multiple critics. The generator is based on a recurrent neural network (RNN) for generating the variable‐length trajectory sequence. The critic uses 1D convolution and 1D adaptive pooling to obtain the trajectory feature. Multiple critics with different structures are used in TGANs‐MC to guide the generator to generate diverse trajectories. Third, a dual‐task network is proposed to find the vessels with abnormal trajectories for warning vessel‐bridge collision and predict the trajectories of vessels for warning vessel‐vessel collision. The dual‐task network adopts the structure of an RNN‐based encoder‐decoder, and it has two branches so that it can jointly perform the dual tasks. The anomaly detection branch uses supervised binary classification and outputs the risk degree. The attention mechanism is adopted in the prediction branch. Finally, a real‐time collision warning system is developed, which is applied on a bridge.