An aerial image data cleaning of power lines with multi-information fusion perception

Abstract

Recently, unmanned aerial vehicles (UAVs) have been used to conduct the task of power line inspection. Unfortunately, most of the images acquired by UAVs are invalid for visual inspection. The main reason is that the collected images are low quality and repetitive. To obtain a set of valid images with high quality, a novel multi-information fusion perception (MIFP) model is proposed to automatically clean the large-scale aerial image data. Firstly, the image quality features and image content features are extracted, in which the weights of different features are used to evaluate the effect on the final image quality. Secondly, the image spatial features are exploited to aggregate spatial information in weight maps of different sizes. Then, the image quality and content features are merged into the multi-information features that characterize the quality of the final image. As a result, the image is picked out according to the quality score. Finally, experimental results show that the proposed multi information fusion perception model has excellent performance on real databases.