Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3

Dai Weicong; Jin Longxu; Li Guoning; Zheng Zhiqiang

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Dai Weicong, Jin Longxu, Li Guoning, et al. Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3[J]. Opto-Electronic Engineering, 2018, 45(12): 180350.

Citation:

Dai Weicong, Jin Longxu, Li Guoning, et al. Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3[J]. Opto-Electronic Engineering, 2018, 45(12): 180350.

Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Changchun University of Science and Technology, Changchun, Jilin 130022, China

Fund Project: Supported by National High Technology Research and Development Program ("863"Program) of China (863-2-5-1-13B)

More Information

Author Bio: Dai Weicong, E-mail: daiweicong16@mails.ucas.ac.cn

Received Date 28 June 2018

Revised Date 22 August 2018

Published Date 01 December 2018

Abstract

Abstract

Focusing on the airplanes in remote-sensing images, a real-time algorithm based on improved YOLOv3 is proposed to detect airplanes in remote-sensing images. Firstly, a convolutional neural network that consists of 49 convolutional layers is proposed to detect airplanes in remote-sensing images specifically. Secondly, dense connection is employed on proposed convolutional neural network, and maxpool is employed to enhance the feature transmit between dense blocks. Finally, to deal with the fact that airplanes in remote-sensing images are small targets mainly, we propose to increase the scale detection from 3 to 4 and employ dense connection to merge feature map among different scales. The algorithm is trained and tested on the designed airplane dataset. The experiment results show that our algorithm obtain 96.26% on precision and 93.81% on recall.
- remote-sensing image /
- airplane target /
- real-time detection /
- convolutional neural network

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References

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Overview

Overview

Overview: The detection of airplanes in remote-sensing images has many important applications in many domains. However, limited to the performance of traditional machine learning methods, the airplanes in remote-sensing images are difficult to be detected. Recently, deep convolutional neural networks are employed to solve object detection problem and reach excellent accuracy. YOLO is one of the most famous real-time object detection algorithms based on regression. Compared with other algorithms, YOLO is more generalized when applied to many domains. Focusing on the airplanes in remote-sensing images, a real-time algorithm based on improved YOLOv3 is proposed to detect airplanes in remote-sensing images. Firstly, a convolutional neural network that consists of 49 convolutional layers is proposed to detect airplanes in remote-sensing images specifically. In the transition blocks of proposed convolutional neural network, we employ 1×1 convolution kernels to further reduce the parameters. Secondly, dense connection is employed on proposed convolutional neural network, and the maxpool is employed to enhance the feature transmit between two dense blocks. In this way, the feature transmit between two dense blocks is reconnected after a undersampling convolutional layer. The dense connection in proposed convolutional neural network enable the network to avoid over-fitting and reach high accuracy although the network is trained by relative few training data. Finally, to deal with the fact that airplanes in remote-sensing images are small targets mainly, we propose to increase the scale detections from 3 to 4 and employ dense connection to merge feature map among different scales. The anchor boxes in our work are obtained by running k-means clustering on the training set bounding boxes. The algorithm is trained and tested on the designed airplane dataset, which have 990 remote-sensing images. The qualitative experiment results show that our algorithm has stronger robustness than other existing algorithms, and our algorithm also shows especially high recall on small targets. The quantitative experiment results show that our algorithm obtains 96.26% on precision, 93.81% on recall and 89.31% on AP. Our algorithm reaches a relative improvement of 13.1% with respect to the YOLOv3 on AP. The detector proposed in this study is proven to perform real-time speed of more than 58.3 frames per second on a 1070Ti GPU. This study demonstrates the high effectiveness and accuracy of deep convolutional neural network in detecting airplanes on remote-sensing images. Meanwhile, the research also shows the fact that the performance of convolutional neural networks is decided by their structure and the number of training data.