An Efficient Target Detection and Recognition Method in Aerial Remote-sensing Images Based on Multiangle Regions-of-Interest

TL;DR

This paper introduces a deep learning-based method for efficient target detection and recognition in aerial remote-sensing images, leveraging multiangle regions-of-interest to improve accuracy over traditional methods.

Contribution

It proposes a novel CNN-based approach combined with a region proposal network to handle multiangle remote-sensing images, enhancing detection precision.

Findings

Achieved higher accuracy than traditional methods

Effectively handles multiangle remote-sensing images

Demonstrates strong applicability in UAV image analysis

Abstract

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.