IEICE TRANSACTIONS on Information
An Improved Insulator and Spacer Detection Algorithm Based on Dual Network and SSD
Summary :
The traditional manual inspection is gradually replaced by the unmanned aerial vehicles (UAV) automatic inspection. However, due to the limited computational resources carried by the UAV, the existing deep learning-based algorithm needs a large amount of computational resources, which makes it impossible to realize the online detection. Moreover, there is no effective online detection system at present. To realize the high-precision online detection of electrical equipment, this paper proposes an SSD (Single Shot Multibox Detector) detection algorithm based on the improved Dual network for the images of insulators and spacers taken by UAVs. The proposed algorithm uses MnasNet and MobileNetv3 to form the Dual network to extract multi-level features, which overcomes the shortcoming of single convolutional network-based backbone for feature extraction. Then the features extracted from the two networks are fused together to obtain the features with high-level semantic information. Finally, the proposed algorithm is tested on the public dataset of the insulator and spacer. The experimental results show that the proposed algorithm can detect insulators and spacers efficiently. Compared with other methods, the proposed algorithm has the advantages of smaller model size and higher accuracy. The object detection accuracy of the proposed method is up to 95.1%.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.662-672
- Publication Date
- 2023/05/01
- Publicized
- 2022/10/17
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DLP0062
- Type of Manuscript
- Special Section PAPER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
- Category
- Smart Industry
Authors
Yong LI
Guangxi University,Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology)
Shidi WEI
Guangxi University
Xuan LIU
Guangxi University
Yinzheng LUO
Guangxi University
Yafeng LI
University of Duisburg-Essen
Feng SHUANG
Guangxi University
Keyword
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Yong LI, Shidi WEI, Xuan LIU, Yinzheng LUO, Yafeng LI, Feng SHUANG, "An Improved Insulator and Spacer Detection Algorithm Based on Dual Network and SSD" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 662-672, May 2023, doi: 10.1587/transinf.2022DLP0062.
Abstract: The traditional manual inspection is gradually replaced by the unmanned aerial vehicles (UAV) automatic inspection. However, due to the limited computational resources carried by the UAV, the existing deep learning-based algorithm needs a large amount of computational resources, which makes it impossible to realize the online detection. Moreover, there is no effective online detection system at present. To realize the high-precision online detection of electrical equipment, this paper proposes an SSD (Single Shot Multibox Detector) detection algorithm based on the improved Dual network for the images of insulators and spacers taken by UAVs. The proposed algorithm uses MnasNet and MobileNetv3 to form the Dual network to extract multi-level features, which overcomes the shortcoming of single convolutional network-based backbone for feature extraction. Then the features extracted from the two networks are fused together to obtain the features with high-level semantic information. Finally, the proposed algorithm is tested on the public dataset of the insulator and spacer. The experimental results show that the proposed algorithm can detect insulators and spacers efficiently. Compared with other methods, the proposed algorithm has the advantages of smaller model size and higher accuracy. The object detection accuracy of the proposed method is up to 95.1%.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLP0062/_p
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@ARTICLE{e106-d_5_662,
author={Yong LI, Shidi WEI, Xuan LIU, Yinzheng LUO, Yafeng LI, Feng SHUANG, },
journal={IEICE TRANSACTIONS on Information},
title={An Improved Insulator and Spacer Detection Algorithm Based on Dual Network and SSD},
year={2023},
volume={E106-D},
number={5},
pages={662-672},
abstract={The traditional manual inspection is gradually replaced by the unmanned aerial vehicles (UAV) automatic inspection. However, due to the limited computational resources carried by the UAV, the existing deep learning-based algorithm needs a large amount of computational resources, which makes it impossible to realize the online detection. Moreover, there is no effective online detection system at present. To realize the high-precision online detection of electrical equipment, this paper proposes an SSD (Single Shot Multibox Detector) detection algorithm based on the improved Dual network for the images of insulators and spacers taken by UAVs. The proposed algorithm uses MnasNet and MobileNetv3 to form the Dual network to extract multi-level features, which overcomes the shortcoming of single convolutional network-based backbone for feature extraction. Then the features extracted from the two networks are fused together to obtain the features with high-level semantic information. Finally, the proposed algorithm is tested on the public dataset of the insulator and spacer. The experimental results show that the proposed algorithm can detect insulators and spacers efficiently. Compared with other methods, the proposed algorithm has the advantages of smaller model size and higher accuracy. The object detection accuracy of the proposed method is up to 95.1%.},
keywords={},
doi={10.1587/transinf.2022DLP0062},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - An Improved Insulator and Spacer Detection Algorithm Based on Dual Network and SSD
T2 - IEICE TRANSACTIONS on Information
SP - 662
EP - 672
AU - Yong LI
AU - Shidi WEI
AU - Xuan LIU
AU - Yinzheng LUO
AU - Yafeng LI
AU - Feng SHUANG
PY - 2023
DO - 10.1587/transinf.2022DLP0062
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - The traditional manual inspection is gradually replaced by the unmanned aerial vehicles (UAV) automatic inspection. However, due to the limited computational resources carried by the UAV, the existing deep learning-based algorithm needs a large amount of computational resources, which makes it impossible to realize the online detection. Moreover, there is no effective online detection system at present. To realize the high-precision online detection of electrical equipment, this paper proposes an SSD (Single Shot Multibox Detector) detection algorithm based on the improved Dual network for the images of insulators and spacers taken by UAVs. The proposed algorithm uses MnasNet and MobileNetv3 to form the Dual network to extract multi-level features, which overcomes the shortcoming of single convolutional network-based backbone for feature extraction. Then the features extracted from the two networks are fused together to obtain the features with high-level semantic information. Finally, the proposed algorithm is tested on the public dataset of the insulator and spacer. The experimental results show that the proposed algorithm can detect insulators and spacers efficiently. Compared with other methods, the proposed algorithm has the advantages of smaller model size and higher accuracy. The object detection accuracy of the proposed method is up to 95.1%.
ER -
English
