Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (8): 994-1003.doi: 10.16183/j.cnki.jsjtu.2021.416

• New Type Power System and the Integrated Energy • Previous Articles     Next Articles

Real-Time Detection of Insulator Drop String Based on UAV Aerial Photography

LI Dengpan1, REN Xiaoming1(), YAN Nannan2,3   

  1. 1. School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
    2. State Grid East China Electric Power Test and Research Institute Co., Ltd., Shanghai 200437, China
    3. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
  • Received:2021-10-18 Online:2022-08-28 Published:2022-08-26
  • Contact: REN Xiaoming


It is of great significance for unmanned aerial vehicle(UAV) to replace manual inspection of power insulators. Aimed at the problem of limited computing power and storage resources of the UAV, an improved real-time target detection algorithm suitable for insulator drop string failure detection is proposed. Based on the YOLOv5s detection network, first, the PANet networks in neck are replaced with bi-directional feature pyramid network(BiFPN) to improve the feature fusion ability. Next, DIoU is used to optimize the loss function to optimize the model. The channel pruning and fine tuning of the γ coefficient generally improve the accuracy, speed, and deployment ability of the detection network. Finally, the image is enhanced at the network output to improve the availability of the algorithm. The proposed algorithm is tested under a specially expanded insulator fault data set. The results show that compared with the original YOLOv5s algorithm, the average accuracy of the proposed algorithm is improved by 3.91%, the detection speed is improved by 25.6%, and the model volume is reduced by 59.1%.

Key words: unmanned aerial vehicle (UAV), insulator drop string, bi-directional feature pyramid network (BiFPN), γ coefficient pruning fine adjustment, DIoU loss function, image enhancement

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