Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 12651-12662 |
| Number of pages | 12 |
| Journal | IEEE Sensors Journal |
| Volume | 23 |
| Issue number | 12 |
| DOIs | |
| State | Published - 15 Jun 2023 |
Keywords
- Convolutional neural network
- deep learning
- lining
- object detection
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