Research on Airplane and Ship Detection of Aerial Remote Sensing Images Based on Convolutional Neural Network.

Changqing Cao,Ting Wang,Xu Yan,Ziqiang Huang,Xiaodong Zeng,Qifan Wu,Jin Wu,Zengyan Wu,Zhejun Feng

doi:10.3390/s20174696

Abstract

The wide range, complex background, and small target size of aerial remote sensing images results in the low detection accuracy of remote sensing target detection algorithms. Traditional detection algorithms have low accuracy and slow speed, making it difficult to achieve the precise positioning of small targets. This paper proposes an improved algorithm based on You Only Look Once (YOLO)-v3 for target detection of remote sensing images. Due to the difficulty in obtaining the datasets, research on small targets for complex images, such as airplanes and ships, is the focus of research. To make up for the problem of insufficient data, we screen specific types of training samples from the DOTA (Dataset of Object Detection in Aerial Images) dataset and select small targets in two different complex backgrounds of airplanes and ships to jointly evaluate the optimization degree of the improved network. We compare the improved algorithm with other state-of-the-art target detection algorithms. The results show that the performance indexes of both datasets are ameliorated by 1–3%, effectively verifying the superiority of the improved algorithm.

Highlights

With the advent of the information age, remote sensing technology has remedied the problem of the limited coverage of traditional ground detection and serious lack of related detection data through rapid air-to-ground information acquisition and target detection
This paper aims to improve the network based on YOLOv3 to further meet the detection requirements for small targets
The experiment uses Faster R-convolutional neural networks (CNNs)+Resnet101, Faster R-CNN+VGG16, YOLOv3, and the improved model based on YOLOv3 Network to train and verify the two aerial remote sensing datasets

Summary

Introduction

With the advent of the information age, remote sensing technology has remedied the problem of the limited coverage of traditional ground detection and serious lack of related detection data through rapid air-to-ground information acquisition and target detection. Due to its significant advantages of flexible maneuverability, high resolution, and optional observation range, aerial remote sensing provides a new method for target detection. Airplanes and ships are vital strategic resources and means of transportation in both military and civilian fields. They are of great significance to the research of remote sensing image detection. Compared with road vehicle detection, remote sensing images of airplanes and ships have complex backgrounds with diverse targets and small sizes, so target detection is more challenging in this context. Before the rise of convolutional neural networks, aerial remote sensing images relied on traditional algorithms for target detection. Traditional target detection algorithms mainly include edge detection algorithms [1], such as the Roberts algorithm [2]; threshold segmentation methods [3], such as the

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