We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Deep Local Multi-level Feature Aggregation Based High-speed Train Image Matching.
- Authors
Jun Li; Xiang Li; Yifei Wei; Xiaojun Wang
- Abstract
At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.
- Subjects
IMAGE registration; DEEP learning; HIGH speed trains; COMPUTER vision; CONVOLUTIONAL neural networks; OPTICAL distortion
- Publication
KSII Transactions on Internet & Information Systems, 2022, Vol 16, Issue 5, p1597
- ISSN
1976-7277
- Publication type
Article
- DOI
10.3837/tiis.2022.05.010