| Citation: | HE Chao, CHEN Guo, WANG Yuwei. Real-time detection method of aero-engine internal damage based on deep learning[J]. Journal of Aerospace Power, 2023, 38（8）：1857-1864 doi: 10.13224/j.cnki.jasp.20210381
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In view of the problem in real-time detection of aero-engine internal damage, an object detection network model based on the YOLOv4 framework was proposed. With advantages of high detection accuracy and fast reasoning speed, this network model realized real-time detection of engine internal damage. In specific implementation process, the method first classified different damage types and annotated the damage location, and then imported the image and its corresponding annotations into the improved network for training to obtain the corresponding detection model. Finally, based on the trained model, real-time detection of damage on pictures and video streams was performed. The Pascal VOC (visual object classes) standard data set and the real aero-engine borescope image data set were used to verify the method. The results showed that the proposed object detection network can improve the frame rate of detection per second more than 23.7% on the premise of ensuring accuracy compared with the original object detection network. The method could provide an effective way to solve the problems of inaccurate detection results and low detection efficiency caused by human factors in borescope damage detection, showing strong engineering practical value.
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