[1]WANG Xiaolin,SU Songzhi,LIU Xiaoying,et al.Cascade convolutional neural networks for airplane detection[J].CAAI Transactions on Intelligent Systems,2020,15(4):697-704.[doi:10.11992/tis.201908028]
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Cascade convolutional neural networks for airplane detection

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 4 Page number: 697-704 Column: 学术论文—机器学习 Public date: 2020-07-05
Title:
Cascade convolutional neural networks for airplane detection
Author(s):
WANG Xiaolin1;  SU Songzhi1;  LIU Xiaoying1;  CAI Guorong2;  LI Shaozi1
1. Intelligent Science & Technology Department, Xiamen University, Xiamen 361005, China;
2. Computer Engineering College, Jimei University, Xiamen 361005, China
Keywords:
airplane detection; remote sensing images; cascade; deep learning; convolutional neural network; two-stage; coarse-to-fine; embedded device
CLC:
TP391.4
DOI:
10.11992/tis.201908028
Abstract:
Detecting airplanes from remote sensing images remains a challenging task, since the images of airplanes always have the characteristics of multiple rotation angles and severe scale change. In order to solve these problems, the most commonly used strategies are to transfer the existing mainstream object detection algorithms based on natural scenario into the remote sensing images directly, such as Faster R-CNN or SSD. However, the backbones of such networks are generally heavy and occupying large space, which are difficult to be applied to low-power consumption devices or front-end embedded systems. To this end, we designed a simple convolutional neural network architecture with only 9 convolutional layers for airplane detection. Our method adopted a coarse-to-fine strategy by cascading a two-stage network, which further reducing the computation cost of detection. Finally, we built a remote sensing dataset for airplane detection to verify our proposed method. The experimental results show that compared with heavy backbone networks such as VGG16, the performance of our method is close to popular methods, but with much less parameters and more than 2 times higher detection speed.
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