In modern power systems, drones are increasingly being utilized to monitor the condition of critical power equipment. However, limited computing capacity is a key factor limiting the application of drones. To optimize the computational load on drones, this paper proposes a cloud–edge collaborative intelligence strategy to be applied to insulator identification and defect detection scenarios. Firstly, a low-computation method deployed at the edge is proposed for determing whether insulator strings are present in the captured images. Secondly, an efficient insulator recognition and defect detection method, I-YOLO (Insulator-YOLO), is proposed for cloud deployment. In the neck network, we integrate an I-ECA (Insulator-Enhanced Channel Attention) mechanism based on insulator characteristics to more comprehensively fuse features. In addition, we incorporated the insulator feature cross fusion network (I-FCFN) to enhance the detection of small-sized insulator defects. Experimental results demonstrate that the cloud–edge collaborative intelligence strategy performs exceptionally well in insulator-related tasks. The edge algorithm achieved an accuracy of 97.9% with only 0.7 G FLOPs, meeting the inspection requirements of drones. Meanwhile, the cloud model achieved a m A P 50 of 96.2%, accurately detecting insulators and their defects.


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    Title :

    Cloud–Edge Collaborative Strategy for Insulator Recognition and Defect Detection Model Using Drone-Captured Images


    Contributors:
    Pengpei Gao (author) / Tingting Wu (author) / Chunhe Song (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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