High-Speed Railway Clearance Intrusion Detection with Improved SSD Network

With the rapid development of high-speed railways, any objects intruding railway clearance will do great threat to railway operations. Accurate and effective intrusion detection is very important. An original Single Shot multibox Detector (SSD) can be used to detect intruding objects except small on...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2019, Vol.9 (15), p.2981
Main Authors: Guo, Baoqing, Shi, Jiafeng, Zhu, Liqiang, Yu, Zujun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Convolution
convolution kernel pruning
deconvolution
Deep learning
High speed rail
Image detection
improved SSD
Intrusion
Intrusion detection
Methods
Neural networks
Pedestrians
Railroads
railway clearance
Sensors
Surveillance
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the rapid development of high-speed railways, any objects intruding railway clearance will do great threat to railway operations. Accurate and effective intrusion detection is very important. An original Single Shot multibox Detector (SSD) can be used to detect intruding objects except small ones. In this paper, high-level features are deconvolved to low-level and fused with original low-level features to enhance their semantic information. By this way, the mean average precision (mAP) of the improved SSD algorithm is increased. In order to decrease the parameters of the improved SSD network, the L1 norm of convolution kernel is used to prune the network. Under this criterion, both the model size and calculation load are greatly reduced within the permitted precision loss. Experiments show that the mAP of our method on PASCAL VOC public dataset and our railway datasets have increased by 2.52% and 4.74% respectively, when compared to the original SSD. With our method, the elapsed time of each frame is only 31 ms on GeForce GTX1060.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9152981