Work

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

Year: 2024

Type: article

Abstract: 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 mAP50 of 96.2%, accurately detecting insulators and their defects.

Source: Drones

DOI: 10.3390/drones8120779

Authors: Pengpei Gao, Tingting Wu, Chunhe Song

Institutions: Shenyang Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, State Key Laboratory of Robotics, China Mobile Research Institute

Language: English

Topic: Power Line Inspection Robots

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,955