期刊
IEEE SENSORS JOURNAL
卷 23, 期 12, 页码 12651-12662出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2023.3274370
关键词
Convolutional neural network; deep learning; lining; object detection
The engine lining plays a crucial role in maintaining the stability of the engine structure by preventing fuel debonding, heat isolation, combustion prevention, and stress buffering. Identifying defects in the formed lining and conducting comprehensive detection is of great significance. This study utilizes an image acquisition system and improved detectors and network components to achieve high-precision identification of image defects.
The engine lining can prevent fuel from debonding with the insulating layer, isolate heat, prevent combustion, and buffer stress. Although its proportion is small, the bonding performance of the lining is directly related to whether the engine can maintain a complete structure and also determines the stability of fuel combustion. How to identify the defects of the formed lining and complete the comprehensive detection of the lining is of great significance to maintain the stability of the engine structure. In this study, an image acquisition system composed of industrial cameras, line-array lenses, and line-light sources is used to achieve the integrity acquisition of the lining surface image. Then, through self-made small-scale data sets for the lining image, the backbone network design, and the improvement of existing detectors and network components, the high-precision identification of image defects is achieved.
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