|Table of Contents|

Airplane Detection in Remote Sensing ImageBased on Faster-RCNN Algorithm(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2018年04期
Page:
79-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Airplane Detection in Remote Sensing ImageBased on Faster-RCNN Algorithm
Author(s):
Zhang Zhongbao; Wang Hongyuan; Zhang Ji; Yang Wei
School of Information Science and Engineering,Changzhou University,Changzhou 213164,China
Keywords:
remote sensing images; airplane detection; Faster-RCNN; residual network; region proposal network; online hard example mining
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2018.04.013
Abstract:
CCCV2017 releases remote sensing image airplane dataset for evaluating airplane detection algorithm. Due to the uncertainty of the orientation of airplanes in remote sensing images and the images with a wide coverage and high background complexity,airplane detection is difficult,the precision and the generalization ability of the model are low. This paper proposes an improved airplane detection algorithm based on Faster-RCNN. First of all,the dataset is reasonably augmented by flipping and rotating the images; then,on the augmented dataset,the residual network is used to extract features from the images and the region proposal network is optimized based on the characteristics of the aspect ratio of airplanes; at the same time,in order to prevent imbalance between positive and negative samples in the training set,the online hard example mining method is used to train the data. The evaluation on the CCCV2017 dataset shows that the improved Faster-RCNN algorithm greatly improved the performance of the initial Faster-RCNN algorithm. In the test set,the mAP(mean Average Precision,mAP)has reached 89.93%. Tests on NWPU VHR-10,NWPU VHR-45,and UCAS-AOD remote sensing image datasets show that the improved model also has good performance,which verifies that the model has good robustness and generalization ability.

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Last Update: 2018-12-30