A typical aircraft engine consists of fans, compressors, turbines, and so on, and each is made of multiple layers of blades. Discovering the site of damages among the large number of blades during aircraft engine maintenance is quite important. However, it is impossible to look directly into the engine unless it is disassembled. For this reason, optical equipment such as a videoscope is used to visually inspect the blades of an engine through inspection holes. The videoscope inspection method has some obvious drawbacks such as the long-time attention on microscopic video feed and high labor intensity. In this research, we developed a damage recognition algorithm using convolutional neural networks and some image-processing techniques related to feature point extraction and matching in order to improve the videoscope inspection method. The image-processing techniques were mainly used for the preprocessing of the videoscope images, from which a suspected damaged region is selected after the preprocessing. The suspected region is finally classified as damaged or normal by the pre-trained convolutional neural networks. We trained the convolutional neural networks 2000 times by using data from 380 images and calculated the classification accuracy using data from 40 images. After repeating the above procedure 50 times with the data randomly divided into training and test groups, an average classification accuracy of 95.2% for each image and a damage detectability of 100% in video were obtained. For verification of the proposed approach, the convolutional neural network part was compared with the traditional neural network, and the preprocessing was compared with the region proposal network of the faster region–based convolutional neural networks. In addition, we developed a platform based on the developed damage recognition algorithm and conducted field tests with a videoscope for a real engine. The damage detection AI platform was successfully applied to the inspection video probed in an in-service engine.