Open AccessObject Detection of Remote Sensing Airport Image Based on Improved Faster R-CNN
Author(s) -
Yongsai Han,
Shiping Ma,
Fei Zhang,
Chenghao Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/3/032010
Subject(s) - softmax function , computer science , artificial intelligence , object detection , pattern recognition (psychology) , classifier (uml) , deep learning , feature (linguistics) , pascal (unit) , computer vision , linguistics , philosophy , programming language
In order to effectively improve the detection accuracy of remote sensing images in airport areas, basing on the representative deep network Faster R-CNN as the object detection method, a deeper basic network ResNet and feature fusion component FPN are used to extract more robust deep distinguishing features, and add a new fully connected layer to the end detection network and combine the softmax classifier and 4 logistic regression classifiers for object detection according to the inter-class correlation of the object. Experiments show that the improvement of the original network brings a 7.7% mAP improvement to 76.6% of the mAP. Compared with other mainstream networks, it also has a better accuracy rate. At the same time, by appropriately reducing the input amount of the proposals, the speed can be increased 3 times to 0.169s under the premise of reducing the accuracy by 2.2%. According to the specific task, the accuracy and detection speed can be reasonably weighed, which reflects the effectiveness and practicability of the network.