Warship Recognition with Semi-Run-Length

• Main Authors: Yu-Chi Huang, 黃鈺棋
• Other Authors: 詹永寬
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5396030%22.&searchmode=basic

碩士 === 國立中興大學 === 資訊管理學系所 === 107 === According to the relations between countries are strained and the awareness of national defense is gradually raised in recent years, the purpose of this research is to develop a warship image recognition system that will virtualize an environment with drones for maritime patrols. However, since warship films or images are difficult to obtain due to national defense military secrets, this study will use the 3D warship model to simulate the image of the warship in the sea environment, hoping to assist our intelligence units to detect and prevent the invasion of enemy ships at sea. This research uses the 3D warship model, including 51 models from six types of ships, to generate images of different perspectives, heights, and distances as a data set so as to apply to identifying the image of the warship. In order to find the features of warship, we use the semi-run-length method to enhance the warship''s color, texture, shape features and other features that can identify the type of warship. In the feature weight parameter part, a genetic algorithm is used to approach the optimization. The experimental results show that the training accuracy rate with 3D warship model can reach 97.6%, and the test accuracy can reach 92.2%. In addition, the paper also identifies the real images captured by a small number of warships at sea and the test accuracy rate is 73.1%.