Aircraft detection in remote sensing images based on saliency and convolution neural network

• Published in: EURASIP journal on wireless communications and networking Vol. 2018; no. 1; pp. 1 - 16
• Main Authors: Hu, Guoxiong, Yang, Zhong, Han, Jiaming, Huang, Li, Gong, Jun, Xiong, Naixue
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2018; SpringerOpen
• Subjects: Algorithms and Architectures for Industrial Wireless Sensor Networks; Communications Engineering; Engineering; Information Systems Applications (incl.Internet); Networks; Remote sensing image, Detection, Saliency, Convolution neural network; Signal,Image and Speech Processing; Remote sensing image, Detection, Saliency, Convolution neural network
• ISSN: 1687-1499; 1687-1499
• DOI: 10.1186/s13638-018-1022-8

New algorithms and architectures for the current industrial wireless sensor networks shall be explored to ensure the efficiency, robustness, and consistence in variable application environments which concern different issues, such as the smart grid, water supply, and gas monitoring. Object detection automatic in remote sensing images has always been a hot topic. Using the conventional deep convolution network based on region proposal for detection, there are many negative samples in the generated region proposal, which will affect the model detection precision and efficiency. Saliency uses the human visual attention mechanism to achieve the bottom-up object detection. Since replacing the selective search with saliency can greatly reduce the number of proposal areas, we will get some region of interests (RoIs) and their position information by using the saliency algorithm based on the background priori for the remote sensing image. And then, the position information is mapped to the feature vector of the whole image obtained by deep convolution neural network. Finally, the each RoI will be classified and fine-tuned bounding box. In this paper, our model is compared with Fast-RCNN that is the current state-of-the-art detection model. The mAP of our model reaches 99%, which is 12.4% higher than that of Fast-RCNN. In addition, we also study the effect of different iterations on model and find the model of 10,000 iterations already has a higher accuracy. Finally, we compare the results of different number of negative samples and find the detection accuracy is highest when the number of negative samples reaches 400.