|Alternative Title||Aircraft Detection and Recognition from Remote-Sensing Images|
|Place of Conferral||中国科学院自动化研究所|
|Keyword||目标检测 飞机 舰船 星载处理器 卷积神经网络 Objection Detection Aircraft Ship On-board Computer Convolutional Neural Network|
|Other Abstract|| With the development of remote sensing satellite imaging technology, remote sensing satellite imaging resolution has been greatly improved. A large number of remote sensing satellites have been successfully launched and a huge amount of image data will be produced, which cannot be read and interpreted by human in a short time. How to carry on the object detection and recognition in remote sensing image is of great importance. At the same time, the deep learning related theory and technology has made a huge progress, and target detection and recognition algorithms got rapid development. Based on in-depth analysis on the present research, this dissertation studied the key techniques of aircraft detection. The main body of our research can be summarized into the following three pieces of work:|
1. Research on the aircraft detection from remote sensing images
For improving the detection precision and efficiency, a remote sensing image aircraft detection method based on the Single Shot MultiBox Detector (SSD) is proposed. To reduce the memory space, an improved version is also proposed. The improved version has a higher efficiency and fewer arguments than the basic one.
2. Research on the multi-categories aircraft detection from remote sensing images
Based on the characteristics of remote sensing image multi-categories detection and recognition and the pros and cons of the existing algorithms, a multi category recognition algorithm based on the SSD is proposed. The method proposed has a higher mAP and efficiency than previous methods.
3. Research on on-board computer based objection detection
n order to meet the requirements of remote sensing image real-time detection, a target detection system is proposed, which is based on the space borne remote sensing image processor. This method implemented the in-orbit target detection, data parsing and a series of functions. The method is based on the specific requirements of the application scenario, which has optimized the detection time on various aspects.
|First Author Affilication||Institute of Automation, Chinese Academy of Sciences|
|巴君. 遥感图像飞机目标检测与识别[D]. 中国科学院自动化研究所. 中国科学院大学,2016.|
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