Remote Sensing Image Target Detection Based on Improved Faster R-CNN

Abstract

Object detection is one of the core tasks of computer vision. With the development of artificial neural networks, object detection has been greatly improved and gradually applied to more fields. In urban traffic, object detection can be used for vehicle detection, autonomous driving, and judging traffic conditions. In the process of navigation, satellite remote sensing images can be used to detect urban vehicles to judge traffic conditions. Object detection can also be used to identify obstacles during car driving. Although accurate detection of larger objects in images has been achieved in many applications, accurate detection of smaller objects remains challenging. The main difficulties in the detection of small targets are low resolution, blurred images, and little information carried. As a result, its feature expression ability is weak, that is, in the process of special zone features, very few features can be extracted, which is not conducive to the detection of small targets. This paper proposes an improved Faster R-CNN algorithm for small target recognition in remote sensing images. In this paper, the feature pyramid structure is used to improve Faster R-CNN. Feature pyramid can perform feature extraction on images of each scale, and can generate multi-scale feature representation, and feature maps of all levels have strong semantic information, even including some high-resolution feature maps. Through the feature pyramid structure, the feature expression ability is enhanced, and the small target feature map resolution is increased at the same time. Secondly, the kmeans clustering transformation algorithm is used to optimize the size of the anchor box to improve the matching degree between the prior box and the ground-truth box. And a channel attention mechanism is introduced in feature fusion to highlight important features and reduce redundant features. Remote sensing images are generally obtained by space platforms, and most of the targets are small targets. The attention mechanism considers both the inter-channel relationship and the spatial position relationship, which enables the model to more accurately identify the target and lock the target position. The attention mechanism improves the accuracy and generalization of small object detection. Finally, we validate the model on RSOD-dataset. Through the experimental verification, the framework proposed in this paper can effectively improve the accuracy of small target detection in aerial remote sensing images, and effectively reduce the false alarm rate, which provides a basis for the research of target detection in aerial remote sensing images.