Abstract
The working condition of power network can significantly influence urban development. Among all the power facilities, electric pylon has an important effect on the normal operation of electricity supply. Therefore, the work status of electric pylons requires continuous and real-time monitoring. Considering the low efficiency of manual detection, we propose to utilize deep learning methods for electric pylon detection in high-resolution remote sensing images in this paper. To verify the effectiveness of electric pylon detection methods based on deep learning, we tested and compared the comprehensive performance of 10 state-of-the-art deep-learning-based detectors with different characteristics. Extensive experiments were carried out on a self-made dataset containing 1500 images. Moreover, 50 relatively complicated images were selected from the dataset to test and evaluate the adaptability to actual complex situations and resolution variations. Experimental results show the feasibility of applying deep learning methods to electric pylon detection. The comparative analysis can provide reference for the selection of specific deep learning model in actual electric pylon detection task.
Highlights
Electricity is one of the most crucial energy supports for economic development and technology progress
This paper introduces deep learning methods to automatically interpret remote sensing images containing electric pylons, which can significantly improve the comprehensive efficiency of electric pylon detection
The detector performing the best average accuracy (AP) during validation was selected as the final detector, and its average precision (AP) gained in testing set was used to evaluate the generalization capability
Summary
Electricity is one of the most crucial energy supports for economic development and technology progress. In the entire power system, electric network is an important link to transfer the electric energy from the power plants with concentrated distribution to individual power users with scattered distribution [1]. In other words, this component of the power system most closely connects with the urban power supply. Field inspections based on unmanned aerial vehicles (UAVs) may show better performance [2,3] This approach proves to be difficult to realize real-time monitoring requirements in the face of large area, and is susceptible to the influence of the surrounding tall buildings. This paper focuses on electric pylon detection in high-resolution remote sensing images captured by satellites
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