IEICE TRANSACTIONS on Information
OPENnet: Object Position Embedding Network for Locating Anti-Bird Thorn of High-Speed Railway
Summary :
Machine vision-based automatic anti-bird thorn failure inspection, instead of manual identification, remains a great challenge. In this paper, we proposed a novel Object Position Embedding Network (OPENnet), which can improve the precision of anti-bird thorn localization. OPENnet can simultaneously predict the location boxes of the support device and anti-bird thorn by using the proposed double-head network. And then, OPENnet is optimized using the proposed symbiotic loss function (SymLoss), which embeds the object position into the network. The comprehensive experiments are conducted on the real railway video dataset. OPENnet yields competitive performance on anti-bird thorn localization. Specifically, the localization performance gains +3.65 AP, +2.10 AP50, and +1.22 AP75.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.824-828
- Publication Date
- 2023/05/01
- Publicized
- 2022/11/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DLL0011
- Type of Manuscript
- Special Section LETTER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
- Category
- Intelligent Transportation Systems
Authors
Zhuo WANG
Beijing Jiaotong University
Junbo LIU
China Academy of Railway Sciences Corporation Limited
Fan WANG
China Academy of Railway Sciences Corporation Limited
Jun WU
Beijing Jiaotong University
Keyword
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Zhuo WANG, Junbo LIU, Fan WANG, Jun WU, "OPENnet: Object Position Embedding Network for Locating Anti-Bird Thorn of High-Speed Railway" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 824-828, May 2023, doi: 10.1587/transinf.2022DLL0011.
Abstract: Machine vision-based automatic anti-bird thorn failure inspection, instead of manual identification, remains a great challenge. In this paper, we proposed a novel Object Position Embedding Network (OPENnet), which can improve the precision of anti-bird thorn localization. OPENnet can simultaneously predict the location boxes of the support device and anti-bird thorn by using the proposed double-head network. And then, OPENnet is optimized using the proposed symbiotic loss function (SymLoss), which embeds the object position into the network. The comprehensive experiments are conducted on the real railway video dataset. OPENnet yields competitive performance on anti-bird thorn localization. Specifically, the localization performance gains +3.65 AP, +2.10 AP50, and +1.22 AP75.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLL0011/_p
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@ARTICLE{e106-d_5_824,
author={Zhuo WANG, Junbo LIU, Fan WANG, Jun WU, },
journal={IEICE TRANSACTIONS on Information},
title={OPENnet: Object Position Embedding Network for Locating Anti-Bird Thorn of High-Speed Railway},
year={2023},
volume={E106-D},
number={5},
pages={824-828},
abstract={Machine vision-based automatic anti-bird thorn failure inspection, instead of manual identification, remains a great challenge. In this paper, we proposed a novel Object Position Embedding Network (OPENnet), which can improve the precision of anti-bird thorn localization. OPENnet can simultaneously predict the location boxes of the support device and anti-bird thorn by using the proposed double-head network. And then, OPENnet is optimized using the proposed symbiotic loss function (SymLoss), which embeds the object position into the network. The comprehensive experiments are conducted on the real railway video dataset. OPENnet yields competitive performance on anti-bird thorn localization. Specifically, the localization performance gains +3.65 AP, +2.10 AP50, and +1.22 AP75.},
keywords={},
doi={10.1587/transinf.2022DLL0011},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - OPENnet: Object Position Embedding Network for Locating Anti-Bird Thorn of High-Speed Railway
T2 - IEICE TRANSACTIONS on Information
SP - 824
EP - 828
AU - Zhuo WANG
AU - Junbo LIU
AU - Fan WANG
AU - Jun WU
PY - 2023
DO - 10.1587/transinf.2022DLL0011
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Machine vision-based automatic anti-bird thorn failure inspection, instead of manual identification, remains a great challenge. In this paper, we proposed a novel Object Position Embedding Network (OPENnet), which can improve the precision of anti-bird thorn localization. OPENnet can simultaneously predict the location boxes of the support device and anti-bird thorn by using the proposed double-head network. And then, OPENnet is optimized using the proposed symbiotic loss function (SymLoss), which embeds the object position into the network. The comprehensive experiments are conducted on the real railway video dataset. OPENnet yields competitive performance on anti-bird thorn localization. Specifically, the localization performance gains +3.65 AP, +2.10 AP50, and +1.22 AP75.
ER -
English
