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

Guoxiong Hu,Naixue Xiong,Jiaming Han,Zhong Yang,Jun Gong,Li Huang

doi:10.1186/s13638-018-1022-8

Abstract

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.

Highlights

With the wireless sensor networks booming, various researches based on wireless sensor networks have made great progress [1,2,3,4,5,6,7,8,9,10], such as the physical sensors [11], architecture for the service computing [12], faulttolerant optimization [13], data gathering and compression [14], and smart data analysis [15]
5 Results and discussion Since the region of interests (RoIs) generated by saliency method are less than those generated by select search method, our algorithm can greatly improve the detection precision and reduce the detection time than the other state-of-the-art algorithms, e.g., RCNN, Fast-RCNN, and Faster-RCNN
Our saliency algorithm based on background prior has a poor effect when the boundaries are fuzzy between the foreground and the background, so it is difficult to obtain some clear salient map if the boundaries are fuzzy, which leads to failure of subsequent object detection

Summary

Introduction

With the wireless sensor networks booming, various researches based on wireless sensor networks have made great progress [1,2,3,4,5,6,7,8,9,10], such as the physical sensors [11], architecture for the service computing [12], faulttolerant optimization [13], data gathering and compression [14], and smart data analysis [15]. We can obtain a large number of high resolution remote sensing images. In the field of remote sensing image processing, it is of great military value for the automatic detection of aircraft in the airport [19]. The blue region and white region are the backgrounds since they significantly touch the image boundary. A small amount of the red region touches the image boundary, but as its size is small, so it looked as a partially cropped object. The green region is clearly a salient object as it is large, compact, and only slightly touches the image boundary. (2)We can obtain salient objects by the ratio of the number of pixels of the part touching the image boundary to those of the entire object field. The part with the smallest ratio is the salient object as shown in Eqs. (1)~(3)

Methods

Results

Conclusion