Multi-scale object detection for high-speed railway clearance intrusion

Clearance intrusion of foreign objects is a great threat to high-speed railway operation safety. An accurate and fast detection system of foreign object intrusion is important. In this paper, a multi-scale foreign object detection algorithm named feature fusion CenterNet with variable focus multi-sc...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2022-03, Vol.52 (4), p.3511-3526
Main Authors: Tian, Runliang, Shi, Hongmei, Guo, Baoqing, Zhu, Liqiang
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Augmentation
Clearances
Computer Science
Datasets
Feature extraction
Feature maps
High speed rail
Intrusion
Machines
Manufacturing
Mechanical Engineering
Object recognition
Processes
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Summary:Clearance intrusion of foreign objects is a great threat to high-speed railway operation safety. An accurate and fast detection system of foreign object intrusion is important. In this paper, a multi-scale foreign object detection algorithm named feature fusion CenterNet with variable focus multi-scale augmentation (FFCN-VFMS) is proposed for the high-speed railway clearance scene. We render the ground truth with two-dimensional Gaussian distributions to generate the confidence score of each region in the image. In addition, a variable focus multi-scale augmentation (VFMS) method is proposed for multi-scale object detection, which takes detection results as prior knowledge to find the range of subsequent detection that contains most small objects. Moreover, feature fusion CenterNet (FFCN) adopts bidirectional iterative deep aggregation (BiIDA) to fuse the features in different convolutional layers and a spatial pyramid pooling (SPP) module to fuse feature maps extracted by different receptive fields. Our method was tested on public PASCAL VOC2007 datasets and our railway clearance intrusion (RCI) datasets. In comparison with related methods, FFCN-VFMS achieves better performance than comparison detectors with respect to accuracy and speed.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-021-02534-9