지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.6
- 수록면
- 772 - 782 (11page)
- DOI
- 10.5370/KIEE.2020.69.6.772
이용수
초록· 키워드
오류제보하기
Over the past few decades the capacity of the power transmission system is growing more and more rapidly, and the power systems have also been expanding in line with continuous expansion of infrastructure. Efficient maintenance of the transmission infrastructure is critical for stable power system operation. However, since the transmission infrastructure is installed at a high location from the ground, the maintenance and diagnosis on transmission towers always involves the possibility of the safety accident of workers. Since 2016, Korea Electric Power Corporation has been developing technologies for monitoring and diagnosis of power lines using drones and has verified the usefulness of technologies through on-site demonstration. 3GBytes of video will be acquired by one flight. And video analysis is performed by below process. ① A worker finds an image frame that includes a transmission tower and power facilities, ② and expands the area of interest in the image, ③ finally the visually observes the facilities and determines whether defects are. These process requires the concentration of the operator and causes very high fatigue. To solve these problems, it is necessary to develop an image analysis algorithm for automatic detection of transmission towers and power facilities to automatically find image frames containing facilities and automatically enlarge the area of interest in the frame to provide diagnosis operator. In this paper describes the process of building a learning and testing image database for detecting transmission towers and facilities carried out for the development of these algorithms and the process of developing algorithms using deep learning CNN-YOLO v2(Convolutional Neural Network - You Only Look Once v2) and the improvement of performance aspects including accuracy and speed with existing algorithms. The recall and precision rate of YOLO v2 for detecting transmission infrastructure is 96.30% and 95.65% respectively, which is more accurate and faster than YOLO v1.
목차
Abstract
1. 서론
2. 기계학습 데이터베이스 구축
3. 송전설비 검출 기계학습 알고리즘 검토 및 선정
4. 실험 결과 및 알고리즘 최적화
5. 결론
References
참고문헌 (12)
참고문헌 신청
Seoktae Kim / 2018 / Patrol Monitoring Plan for Transmission Towers with a Commercial Drone and its Field Tests / KEPCO Journal on Electric Power and Energy 4
Taskeed Jabid / 2016 / Rotation invariant power line insulator detection using local directional pattern and support vector machine / Proc. of 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET)
Ren, Shaoqing / 2015 / Faster r-cnn: Towards real-time object detection with region proposal networks / Advances in Neural Information Processing Systems
Nal Kalchbrenner / 2014 / A convolutional neural network for modelling sentences / arXiv preprint arXiv : 1404. 2188
Joseph Redmon / 2016 / You only look once: Unified, realtime object detection / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Taskeed Jabid / 2016 / Rotation invariant power line insulator detection using local directional pattern and support vector machine / Proc. of 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET)
Ren, Shaoqing / 2015 / Faster r-cnn: Towards real-time object detection with region proposal networks / Advances in Neural Information Processing Systems
Nal Kalchbrenner / 2014 / A convolutional neural network for modelling sentences / arXiv preprint arXiv : 1404. 2188
Joseph Redmon / 2016 / You only look once: Unified, realtime object detection / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
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