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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\u2019 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.<\/jats:p>","DOI":"10.1155\/2020\/3107450","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T02:20:15Z","timestamp":1601518815000},"page":"1-11","source":"Crossref","is-referenced-by-count":30,"title":["Analysis on the Impact of Data Augmentation on Target Recognition for UAV-Based Transmission Line Inspection"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8392-1777","authenticated-orcid":true,"given":"Chunhe","family":"Song","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxiang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongfeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shimao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7863-3260","authenticated-orcid":true,"given":"Peng","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9524-7609","authenticated-orcid":true,"given":"Zhaojie","family":"Ju","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.2018.2871750"},{"key":"2","first-page":"91","article-title":"Faster R-CNN: towards real time object detection with region proposal networks","volume":"50","author":"S. 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