Name
Abstract | ||
|---|---|---|
Automatic aircraft engine defect detection is a challenging but important task in industry which can ensure safe air transportation and flight. In this paper, we propose a fast and accurate feature weighting network (FWNet) to solve the problem of defect scale variation and improve detection accuracy. The framework is designed based on recent popular convolutional neural networks and feature pyramid. To further boost the representation power of the network, a new feature weighting module (FWM) was proposed to recalibrate the channel-wise attention and increase the weights of valid features. The model was trained and tested on a self-built dataset, which consisted of 1916 images and contained three defect types: ablation, crack and coating missing. Extensive experimental results verify the effectiveness of the proposed FWM and show that the proposed method can accurately detect engine defects of different scales and different locations. Our method obtains 89.4% mAP and can run at 6FPS, which surpasses other state-of-the-art detection methods and can quickly provide diagnostic basis for aircraft maintenance inspectors in practical applications. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1142/S0219691320500125 | INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING |
Keywords | DocType | Volume |
Defect detection, scale variation, convolutional neural networks, feature weighting module | Journal | 18 |
Issue | ISSN | Citations |
3 | 0219-6913 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liqiong Chen | 1 | 1 | 1.37 |
| Lian Zou | 2 | 1 | 2.38 |
| Cien Fan | 3 | 1 | 3.06 |
| Yifeng Liu | 4 | 0 | 3.72 |