Quality assessment for inspection images of power lines based on spatial and sharpness evaluation

Abstract

Digital imaging and image-processing techniques have revolutionized the way of power line inspection in recent years. Massive images are captured and utilized for further processing to maintain the reliability, safety, and sustainability of power transmission. For power line inspection, the component region in the delivered images is required to be centered, large, and clear enough. In this paper, a component-oriented image quality assessment method is proposed to automatically predict image quality according to the demand of power line inspection. The proposed method considers two factors: spatial characteristic evaluation and sharpness evaluation. The spatial characteristic evaluation utilizes YOLOv3 to evaluate whether the component region is sufficiently centered and large, which enables the observer to quickly find the target. The sharpness evaluation employs ResNet to evaluate the clarity of component and makes the condition monitoring more accurately. For final quality assessment, a multi-stage filtering strategy is presented to aggregate these two factors and obtain high quality inspection images. The experimental results indicate that the high-quality images can be accurately identified to satisfy the requirements of power line inspection. The proposed quality assessment method enhances the efficiency for further data analysis of aerial images.