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A warning framework for avoiding vessel-bridge and vessel-vessel collisions based on generative adversarial and dual-task networks
Computer-Aided Civil and Infrastructure Engineering ( IF 9.6 ) Pub Date : 2021-09-09 , DOI: 10.1111/mice.12757
Bo Zhang 1 , Zhaofeng Xu 1, 2 , Jian Zhang 1 , Gang Wu 1
Affiliation  

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.

中文翻译:

基于生成对抗和双任务网络的避免船桥和船船碰撞的警告框架

船桥和船船碰撞很可能发生在河流上。为了避免这两种碰撞,本文提出了一种基于视频的预警框架,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实时血管定位方法。该方法以浮标为控制点,根据航拍获取其瞬时世界坐标,求解单应性。根据得到的单应性,可以将识别出的血管中心的像素坐标映射到对应的世界坐标,从而实现血管定位。第二,提出了具有多个批评者的轨迹生成对抗网络(TGANs-MC)来丰富历史轨迹,尤其是异常轨迹。TGANs-MC 包含一个生成器和多个批评者。生成器基于循环神经网络 (RNN),用于生成可变长度轨迹序列。critic 使用 1D 卷积和 1D 自适应池化来获得轨迹特征。TGANs-MC 中使用了多个具有不同结构的评论家来指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船舶进行预警船桥碰撞,并预测船舶轨迹以预警船船碰撞。双任务网络采用基于RNN的encoder-decoder结构,它有两个分支,因此可以共同执行双重任务。异常检测分支使用有监督的二元分类并输出风险程度。在预测分支中采用注意机制。最后,开发了一种实时碰撞预警系统,并应用于一座桥梁。
更新日期:2021-09-09
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