Developing a new red band–SEVI–blue band (RSB) enhancement method for recognition the extra-high-voltage transmission line corridor in green mountains

Hong, Jin; Zhang, Yong; Lin, Jinglan; Zheng, Xiaogan; Yue, Hui; Chen, Yunzhi

doi:10.1080/17538947.2023.2184511

Cited by 1 publication

(1 citation statement)

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“…When isolators are exposed to the external environment for a long period, they are susceptible to the environmental climate and other factors that can lead to rusting and breakages. To ensure a stable power line operation, insulators in power lines must be inspected regularly to eliminate fault factors early and minimize their impact [1][2][3]. The maturity of drone inspection technology has greatly improved the efficiency of power line inspection while placing immense strain on the work of inspection.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Detection of Transmission Line Insulators

Zhang

Tian

et al. 2023

Energies

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At this stage, the inspection of transmission lines is dominated by UAV inspection. Insulators, as essential equipment for transmission line equipment, are susceptible to various factors during UAV detection, and their detection results often lead to leakages and false detection. Combining deep learning detection algorithms with the UAV transmission line inspection system can effectively solve the current sensing problem. To improve the recognition accuracy of insulator detection, the MS-COCO pre-training strategy that combines the FPN module with a cascading R-CNN algorithm based on the ResNeXt-101 network is proposed. The purpose of this paper is to systematically and comprehensively analyze mainstream isolator detection algorithms at the current stage and to verify the effectiveness of the improved Cascade R-CNN X101 model by combining the mAP (mean Average Precision) value and other related evaluation indices. Compared with Faster R-CNN, Retina Net, and other detection algorithms, the model is highly accurate and can effectively deal with the false detection, leakage, and non-recognition of the environment in online special detection. The research in this paper provides a new idea for intelligent fault detection of transmission line insulators and has some reference value for engineering applications.

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Section: Introductionmentioning

confidence: 99%