3. 针对舰船航向航速数据集匮乏以及舰船尾迹结构复杂导致航向航速难分析问题，本文构建了一个基于遥感影像的航向航速估计数据集并在此基础上针对开尔文尾迹舰船目标提出了航向航速自动估计算法。在航向估计方面，本文在旋转矩形框的检测结果上结合舰船首尾的纹理分析提出了航向自动估计方法，与传统基于 Radon 变换的舰船航向提取方法进行了对比，证明了所提方法的优越性。在航速估计方面，本文充分分析了舰船航速和开尔文尾迹频域内波高谱的关系，提出了一种航速自动估计方法，并和类似方法进行了对比，证明了本文方法的有效性。
Maritime ship activity monitoring is of great significance for our country to safeguard maritime rights and interests and guarantee maritime security, and is widely used in implementing maritime seizure, combating illegal and criminal activities, emergency rescue, fisheries management, etc. The automatic ship identification system based on radio signals is a common way for ship activity monitoring, but it often loses its expected function when facing non-cooperative ships, so there is an urgent need for other effective means to accurately locate and estimate their status. Optical remote sensing satellite images have the advantages of wide observation range, high spatial resolution, strong timeliness and relatively easy interpretation, and their use for ship activity monitoring largely makes up for the shortage of ship automatic identification system and greatly strengthens the monitoring means of relevant departments. The combination of the two can not only understand the current position and movement status of the ship, but also predict the future position of the ship, which is an important support for the analysis of the ship's activity pattern and movement prediction.
The current remote sensing image-based maritime ship detection and heading speed estimation methods are still far from the actual application requirements. On the one hand, the remote sensing images of maritime scenes are characterized by large size and extremely sparse distribution of ship targets. When using deep learning methods to process large size remote sensing images, the detection is mostly carried out window by window by sliding window, and the detection speed can hardly meet the demand of timely processing of massive remote sensing image data. On the other hand, as the main feature of moving ships in static remote sensing images, the wake is often used as an important analysis object for heading speed estimation, but its morphological structure is complex and the corresponding dataset is scarce, and most of the existing researches are based on simulated dataset, which is not effective on real data.
To address the current problems faced by maritime ship detection and heading speed estimation, the work of this thesis is summarized as follows:
1. In order to improve the efficiency of ship detection at sea and enhance the accuracy of heading estimation, this thesis combines the ship detection task and heading estimation task, and improves on YOLOv5 by proposing a detection model based on rotating rectangular frame, YOLOv5-Rotation, which has comparable accuracy but about 2 to 4 times faster than similar detection models on self-built and public ship detection datasets, while the average ship orientation deviation on the self-built dataset is less than 4∘.
2. To further speed up the detection of sparse ship targets in large-size remote sensing images of maritime scenes, this thesis proposes a fast ship detection method based on classification-detection multi-task learning. The method reduces the redundant computation of the whole remote sensing image by introducing classifier branches in the shallow layer of the detection network model to determine whether there are targets in the window, i.e., to achieve pre-screening of simple background windows. On the self-built large-size maritime ship dataset, the method improves the detection speed by about 30% compared with the original detection network while maintaining comparable accuracy.
3. To address the lack of ship heading speed dataset and the difficulty of analyzing the heading speed due to the complex structure of the ship's wake, this thesis constructs a remote sensing image-based heading speed estimation dataset and proposes an automatic heading speed estimation algorithm for Kelvin wake ship targets based on this dataset. In terms of heading estimation, this thesis proposes an automatic heading estimation algorithm based on the detection results of the rotating rectangular frame combined with the texture analysis of the ship's bow and stern, and compares it with the traditional Radon transform-based ship heading extraction method to prove the superiority of the proposed method. In terms of speed estimation, this thesis fully analyzes the relationship between ship speed and Kelvin wake frequency domain wave height spectrum, proposes an automatic speed estimation method, and compares it with other methods to demonstrate the effectiveness of this method.
|Keyword||海上舰船检测 舰船航向估计 舰船航速估计|
|姚东盼. 基于光学遥感影像的海上舰船检测及航向航速估计[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.|
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