An Efficient Target Detection and Recognition Method in Aerial Remote-sensing Images Based on Multiangle Regions-of-Interest
This addresses the challenge of efficiently analyzing remote-sensing images for target identification and position calculation, which is crucial for applications in areas like surveillance or mapping, but it appears incremental as it adapts existing deep learning techniques to a specific domain.
The paper tackled the problem of target detection and recognition in aerial remote-sensing images from UAVs, which differ from ordinary pictures in shooting angles and methods, by proposing a method based on deep CNN with multiangle regions-of-interest, resulting in much more accurate and precise results than traditional ways.
Recently, deep learning technology have been extensively used in the field of image recognition. However, its main application is the recognition and detection of ordinary pictures and common scenes. It is challenging to effectively and expediently analyze remote-sensing images obtained by the image acquisition systems on unmanned aerial vehicles (UAVs), which includes the identification of the target and calculation of its position. Aerial remote sensing images have different shooting angles and methods compared with ordinary pictures or images, which makes remote-sensing images play an irreplaceable role in some areas. In this study, a new target detection and recognition method in remote-sensing images is proposed based on deep convolution neural network (CNN) for the provision of multilevel information of images in combination with a region proposal network used to generate multiangle regions-of-interest. The proposed method generated results that were much more accurate and precise than those obtained with traditional ways. This demonstrated that the model proposed herein displays tremendous applicability potential in remote-sensing image recognition.