10 April 2018 Efficient airport detection using region-based fully convolutional neural networks
Author Affiliations +
Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061502 (2018) https://doi.org/10.1117/12.2302952
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Peng Xin, Peng Xin, Yuelei Xu, Yuelei Xu, Xulei Zhang, Xulei Zhang, Shiping Ma, Shiping Ma, Shuai Li, Shuai Li, Chao Lv, Chao Lv, "Efficient airport detection using region-based fully convolutional neural networks", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061502 (10 April 2018); doi: 10.1117/12.2302952; https://doi.org/10.1117/12.2302952
PROCEEDINGS
7 PAGES


SHARE
Back to Top