Abstract:

Objectives Timely and accurate detection of power facilities is very important to ensure the reliability of energy supply. However, a single sensor has certain limitations in the detection of power facilities. Therefore, a multi-scale feature heterogeneous image fusion algorithm based on saliency detection was proposed. Methods Firstly, the edge guidance network was used to extract the salient target from the infrared image to generate the salient target mask. Secondly, a specific loss function was established in each region, and the salient target mask was used to guide the network for feature extraction. Finally, a directional heterogeneous fusion method based on feature hierarchy was proposed, which combined the depth features of different scales to minimize information loss. Results Subjective and objective experiments on the TNO dataset show that the algorithm is superior to other methods in most evaluation indicators, which verifies the effectiveness of its application in the field of power facilities detection. Conclusions The algorithm effectively solves the problems of low detection rate and information loss, and makes the detection of power facilities more comprehensive and accurate. It is of great significance to improve the accuracy and diagnostic efficiency of power equipment fault detection.

Key words: power facility detection, heterogeneous image fusion, target detection, deep learning