![](/images/graphics-bg.png)
Analysis on the Impact of Data Augmentation on Target Recognition for UAV-Based Transmission Line Inspection
Joint Authors
Wang, Zhongfeng
Song, Chunhe
Xu, Wenxiang
Yu, Shimao
Zeng, Peng
Ju, Zhaojie
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-30
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Target recognition is one of the core tasks of transmission line inspection based on Unmanned Aerial Vehicle (UAV), and at present plenty of deep learning-based methods have been developed for it.
To enhance the generalization ability of the recognition models, a huge number of training samples are needed to cover most of all possible situations.
However, due to the complexity of the environmental conditions and targets, and the limitations of images’ collection and annotation, the samples usually are insufficient when training a deep learning model for target recognition, which is one of the main factors reducing the performance of the model.
To overcome this issue, some data augmentation methods have been developed to generate additional samples for model training.
Although these methods have been widely used, currently there is no quantitative study on the impact of the data augmentation methods on target recognition.
In this paper, taking insulator strings as the target, the impact of a series of widely used data augmentation methods on the accuracy of target recognition is studied, including histogram equalization, Gaussian blur, random translation, scaling, cutout, and rotation.
Extensive tests are carried out to verify the impact of the augmented samples in the training set, the test set, or the both.
Experimental results show that data augmentation plays an important role in improving the accuracy of recognition models, in which the impacts of the data augmentation methods such as Gaussian blur, scaling, and rotation are significant.
American Psychological Association (APA)
Song, Chunhe& Xu, Wenxiang& Wang, Zhongfeng& Yu, Shimao& Zeng, Peng& Ju, Zhaojie. 2020. Analysis on the Impact of Data Augmentation on Target Recognition for UAV-Based Transmission Line Inspection. Complexity،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1141308
Modern Language Association (MLA)
Song, Chunhe…[et al.]. Analysis on the Impact of Data Augmentation on Target Recognition for UAV-Based Transmission Line Inspection. Complexity No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1141308
American Medical Association (AMA)
Song, Chunhe& Xu, Wenxiang& Wang, Zhongfeng& Yu, Shimao& Zeng, Peng& Ju, Zhaojie. Analysis on the Impact of Data Augmentation on Target Recognition for UAV-Based Transmission Line Inspection. Complexity. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1141308
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141308