台灣鄰近海域常見軍艦影像辨識系統之建構

Taiwan has been facing the threats from China and need to improve the capability of identification of naval ships. This research use convolution neural network (CNN) to establish a system for this purpose. A database of total 1743 images of real ships were established including 20 types of aircraft carrier or landing craft related ships from 8 countries. Image processing techniques, such as shifting, contrast enhancing, adding Gaussian noise, and fogging, were used to expand the image database to 8715 images. Four networks were used for training, including GoogLeNet (Inception-v1), Inception-v3, ResNet-18, and ResNet-101. The identification rate were compared for normal and foggy weather using these four networks. The results show that the system's best identification rate is 96.3% in normal weather condition, and 89.5% in foggy condition. It is especially good for identifying American and China's aircraft carries. But poor at Japan and S. Korea's large amphibious assault ships.