Power Devices and Abnormal Object Detection of Transmission Lines Based on Improved RetinaNet Algorithm

doi:10.3969/j.issn.1008- 0198.2023.05.012

Hunan Electric Power ›› 2023, Vol. 43 ›› Issue (5): 79-84.doi: 10.3969/j.issn.1008- 0198.2023.05.012

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Power Devices and Abnormal Object Detection of Transmission Lines Based on Improved RetinaNet Algorithm

PENG Ziyang¹, CHEN Nuotian¹, YI Junfei¹, TAO Ziming¹, MAO Jianxu¹, XIE Jinying²

1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;
2. State Grid Changsha Power Supply Company,Changsha 410015, China

Received:2023-06-14 Revised:2023-07-24 Online:2023-10-25 Published:2023-11-03

Abstract

Abstract: In this paper,aiming at the problems that power equipment may be occluded and the detection target is small and difficult to identify, adaptive training sample selection strategy is adopted to solve the equipment occlusion problem. Additionally, the CIoU loss function is utilized to overcome difficulties in detecting small targets, further enhancing detection accuracy. Furthermore, data augmentation methods are employed to increase algorithm robustness and optimize detection performance.Both theoretical analysis and practical results demonstrate that the optimized algorithm significantly improves the accuracy of detecting power equipment and abnormal targets in transmission lines, thus achieving effective dectection of power components and abnormal targets on transmission lines.

Key words: power operation scenarios, CIoU loss function, data augmentation, transmission line power devices, abnormal target detection

CLC Number:

TM755

PENG Ziyang, CHEN Nuotian, YI Junfei, TAO Ziming, MAO Jianxu, XIE Jinying. Power Devices and Abnormal Object Detection of Transmission Lines Based on Improved RetinaNet Algorithm[J]. Hunan Electric Power, 2023, 43(5): 79-84.

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Power Devices and Abnormal Object Detection of Transmission Lines Based on Improved RetinaNet Algorithm

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