Using unmanned aerial vehicles (UAVs) for equipment condition monitoring is an important application of industrial Internet of things (IIoT), and the limited energy is the key factor to restrict the application of UAV. In order to reduce the computational load for intelligence computing of UAV, this paper proposes a cloud edge collaborative intelligent method for object detection, and applies it to insulator string recognition defect detection in the power IIoT. First, the impact of the extremely large aspect ratio of object on the detection accuracy and the computational load are analyzed, then the cloud edge collaborative intelligent method for insulator string detection and defect recognition is presented, in which on the UAV side a low cost method is proposed for estimating possible directions of insulator strings, and on the cloud side an effective method is proposed for insulator string defect detection. The experimental results show the effectiveness of the proposed algorithm. To the best knowledge of us, this paper is the first work to analyze the impact of the extremely large aspect ratio of insulator string on the detection accuracy and the computational load.

This work is published on IEEE Internet of Things Journal 8.9(2021):7510-7520.