MSSIF-Net: an efficient CNN automatic detection method for freight train images-Reference-Cited by-同舟云学术

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MSSIF-Net: an efficient CNN automatic detection method for freight train images

Published:2022-11-29 Issue:9 Volume:35 Page:6767-6785
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Zhang Longxin^ORCID,Hu Yang,Chen Jingsheng,Li Chuang,Li Keqin

Funder

National Key Research and Development Program of China

Young Scientists Fund

Natural Science Foundation of Hunan Province

Research Foundation of Education Bureau of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-08035-1.pdf

Reference41 articles.

1. Sun J, Xiao Z, Xie Y (2017) Automatic multi-fault recognition in tfds based on convolutional neural network. Neurocomputing 222:127–136

2. Fu X, Li K, Liu J, Li K, Zeng Z, Chen C (2020) A two-stage attention aware method for train bearing shed oil inspection based on convolutional neural networks. Neurocomputing 380:212–224

3. De Bruin T, Verbert K, Babuška R (2016) Railway track circuit fault diagnosis using recurrent neural networks. IEEE Trans Neural Netw Learn Syst 28(3):523–533

4. Zhang Y, Liu M, Yang Y, Guo Y, Zhang H (2021) A unified light framework for real-time fault detection of freight train images. IEEE Trans Industr Inf 17(11):7423–7432

5. Leng J, Liu Y (2021) Single-shot augmentation detector for object detection. Neural Comput Appl 33(8):3583–3596

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