Key parts of transmission line detection using improved YOLO v3
Joint Authors
Ming, Gao
Renwei, Tu
Zhongjie, Zhu
Yongqiang, Bai
Zhifeng, Ge
Source
The International Arab Journal of Information Technology
Issue
Vol. 18, Issue 6 (30 Nov. 2021), pp.747-754, 8 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2021-11-30
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Unmanned Aerial Vehicle (UAV) inspection has become one of main methods for current transmission line inspection, but there are still some shortcomings such as slow detection speed, low efficiency, and inability for low light environment.
To address these issues, this paper proposes a deep learning detection model based on You Only Look Once (YOLO) v3.
On the one hand, the neural network structure is simplified, that is the three feature maps of YOLO v3 are pruned into two to meet specific detection requirements.
Meanwhile, the K-means++ clustering method is used to calculate the anchor value of the data set to improve the detection accuracy.
On the other hand, 1000 sets of power tower and insulator data sets are collected, which are inverted and scaled to expand the data set, and are fully optimized by adding different illumination and viewing angles.
The experimental results show that this model using improved YOLO v3 can effectively improve the detection accuracy by 6.0%, flops by 8.4%, and the detection speed by about 6.0%.
American Psychological Association (APA)
Renwei, Tu& Zhongjie, Zhu& Yongqiang, Bai& Ming, Gao& Zhifeng, Ge. 2021. Key parts of transmission line detection using improved YOLO v3. The International Arab Journal of Information Technology،Vol. 18, no. 6, pp.747-754.
https://search.emarefa.net/detail/BIM-1430929
Modern Language Association (MLA)
Renwei, Tu…[et al.]. Key parts of transmission line detection using improved YOLO v3. The International Arab Journal of Information Technology Vol. 18, no. 6 (Nov. 2021), pp.747-754.
https://search.emarefa.net/detail/BIM-1430929
American Medical Association (AMA)
Renwei, Tu& Zhongjie, Zhu& Yongqiang, Bai& Ming, Gao& Zhifeng, Ge. Key parts of transmission line detection using improved YOLO v3. The International Arab Journal of Information Technology. 2021. Vol. 18, no. 6, pp.747-754.
https://search.emarefa.net/detail/BIM-1430929
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 753-754
Record ID
BIM-1430929