Automatic Object Detection on Aerial Images Using Convolutional Neural Networks (International Journal of Innovative Research in Computer Science & Technology, 2021)
Large quantities of aerial and satellite  images are being acquired on a daily basis. Many  practical applications may benefit from the analysis of  such huge amounts of data. We propose an automated  content-based analysis of aerial photography in this letter,  which may be used to identify and label arbitrary objects  or areas in high-resolution pictures. We developed a  convolutional neural network-based approach for  automated object identification for this purpose. In the  tasks of aerial picture classification and object  identification, a new two-stage method for network  training is developed and validated. First, we used the UC  Merced data set of aerial pictures to evaluate the  suggested training method, and we were able to obtain an  accuracy of about 98.6%. Second, a technique for  automatically detecting objects was developed and tested.  For GPGPU implementation, a processing time of  approximately 30 seconds was needed for one aerial  picture of size 5000 x 5000 pixels.
Keywords: Aerial images; Automatic; Convolutional Neural Networks (CNNs); Convolutional neural network; Object detection