TY - GEN
T1 - Research on the Segmentation and Extraction of Scenes Along Railway Lines Based on Remote Sensing Images of UAVs
AU - Tong, Lei
AU - Jia, Limin
AU - Wang, Zhipeng
AU - Wu, Yunpeng
AU - Wang, Ning
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2020.
PY - 2020
Y1 - 2020
N2 - At present, the manual inspection along railway lines is still a major method to ensure railway operation safely, but the cost is high and work efficiency is low. Therefore, unmanned aerial vehicles (UAVs) patrol inspection is required. This paper presents the effective segmentation of scenes along railway lines (SRL) from remote sensing perspective of UAVs based on the full convolutional networks (FCN). Firstly, the datasets needed in this research are collected and produced from Langfang section of the Beijing–Shanghai high-speed railway. The datasets are expanded by using data augmentation to constrain the overfitting in the training process. Secondly, the segmentation model FCN-8s for SRL is developed and trained. The related setting and hardware environment in the training process are described in this paper. The experimental results show that a single image prediction needs 151.2 ms, to achieve 6.6 fps when input size is 384 × 384. Good accuracy is obtained on the test dataset, i.e., 55.8% MIoU and 70.2% MPA, which meets the expectations of FCN. At the same time, it is also found that the segmentation of railway area achieves the best result thus the railway area is extracted accordingly.
AB - At present, the manual inspection along railway lines is still a major method to ensure railway operation safely, but the cost is high and work efficiency is low. Therefore, unmanned aerial vehicles (UAVs) patrol inspection is required. This paper presents the effective segmentation of scenes along railway lines (SRL) from remote sensing perspective of UAVs based on the full convolutional networks (FCN). Firstly, the datasets needed in this research are collected and produced from Langfang section of the Beijing–Shanghai high-speed railway. The datasets are expanded by using data augmentation to constrain the overfitting in the training process. Secondly, the segmentation model FCN-8s for SRL is developed and trained. The related setting and hardware environment in the training process are described in this paper. The experimental results show that a single image prediction needs 151.2 ms, to achieve 6.6 fps when input size is 384 × 384. Good accuracy is obtained on the test dataset, i.e., 55.8% MIoU and 70.2% MPA, which meets the expectations of FCN. At the same time, it is also found that the segmentation of railway area achieves the best result thus the railway area is extracted accordingly.
KW - Railway scenes segmentation
KW - Semantic segmentation
KW - UAVs
UR - https://www.scopus.com/pages/publications/85085733588
U2 - 10.1007/978-981-15-2866-8_47
DO - 10.1007/978-981-15-2866-8_47
M3 - 会议稿件
AN - SCOPUS:85085733588
SN - 9789811528651
T3 - Lecture Notes in Electrical Engineering
SP - 481
EP - 492
BT - Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation, EITRT 2019 - Rail Transportation System Safety and Maintenance Technologies
A2 - Qin, Yong
A2 - Jia, Limin
A2 - Liu, Baoming
A2 - Liu, Zhigang
A2 - Diao, Lijun
A2 - An, Min
PB - Springer
T2 - 4th International Conference on Electrical and Information Technologies for Rail Transportation, EITRT 2019
Y2 - 25 October 2019 through 27 October 2019
ER -