Real-Time Detection of Insulator Drop String Based on UAV Aerial Photography
| Autor: | LI Dengpan, REN Xiaoming, YAN Nannan |
|---|---|
| Jazyk: | čínština |
| Rok vydání: | 2022 |
| Předmět: |
unmanned aerial vehicle (uav)
insulator drop string bi-directional feature pyramid network (bifpn) γ coefficient pruning fine adjustment diou loss function image enhancement Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989 |
| Zdroj: | Shanghai Jiaotong Daxue xuebao, Vol 56, Iss 8, Pp 994-1003 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1006-2467 |
| DOI: | 10.16183/j.cnki.jsjtu.2021.416 |
| Popis: | 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%. |
| Databáze: | Directory of Open Access Journals |
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