本文为美国南佛罗里达大学（作者：Sergiy Fefilatyev）的硕士论文，共72页。

本文提出了一种新的海上船只图像和视频自动检测技术。该系统的用户包括边防、军事、港口安全、流量管理和避难所保护人员。图像和视频的来源是数码相机或摄像机，安装在浮标或固定在港口设施上。该系统旨在通过自主工作，拍摄周围海洋表面的图像，并对其进行分析，以确定是否存在海上船只。该系统的目标是检测船只周围的一个近似窗口。

本文提出了一种基于计算机视觉的边缘检测和后处理相结合的水平检测方法。利用静止图像的多个数据集来评价该技术的性能。对于视频序列，使用卡尔曼滤波的跟踪算法进一步增强了原始算法。由30个视频序列组成的独立数据集，每个视频序列长度为10秒，该数据集用于测试其性能。最后，讨论了船只检测的应用前景，并提出了改进措施。

This work presents a new technique for automatic detection of marine vehicles in images and video of open sea. Users of such system include border guards, military, port safety, flow management, and sanctuary protection personnel. The source of images and video is a digital camera or a camcorder which is placed on a buoy or stationary mounted in a harbor facility. The system is intended to work autonomously, taking images of the surrounding ocean surface and analyzing them for the presence of marine vehicles. The goal of the system is to detect an approximate window around the ship. The proposed computer vision-based algorithm combines a horizon detection method with edge detection and postprocessing. Several datasets of still images are used to evaluate the performance of the proposed technique. For video sequences the original algorithm is further enhanced with a tracking algorithm that uses Kalman filter. A separate dataset of 30 video sequences 10 seconds each is used to test its performance. Promising results of the detection of ships are discussed and necessary improvements for achieving better performance are suggested.

1 引言

2 项目背景

3 算法研究

4 数据集与性能评估

5 水平检测算法比较

6 海面船只检测的结果

7 结论 

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