A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

Abstract

Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.

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“…Jalil et al applied the multi-modal sensors to capture the visible and infrared images and detect the faults or damaged components of the transmission infrastructure [17]. Han et al aimed to detect the insulator faults from aerial images [18]. A manually labelled dataset was built for the newly designed convolutional network to solve the insulator faults detection problem.…”

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“…In 2004, Canadian began to use the UAV-mounted photoelectric pods to carry out highvoltage transmission line inspections, which greatly reduce the cost of inspection. In [5], a novel method is proposed for insulator fault detection, which can detect both one fault and multi-fault in UAV-based aerial images.…”

“…The other improvement is to use max k∈S x gk to replace h∈S x gh in the classical model, so as to ensure that the result remains 1 even if the hangar is revisited many times. The hangar nodes are introduced into the TSP model through constraints (5), so that the form can be maintained consistent with the classical TSP model without the hangar node, and it is not compulsory that the hangar node must be visited. Constraints (6) track UAV's power level, indicating the relationship between the power of the preceding node and the following node.…”

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“…It also solved the problem of missed detection caused by insulator shielding. Han et al[84] designed a new neural network to detect insulators. There was high average precision but low real-time performance in the model.…”