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      Infrared Target Detection and Recognition Method in Airborne Photoelectric System

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          Abstract

          Infrared target detection and recognition are investigated by considering the wide application requirements of an airborne photoelectric system. The proposed algorithm can be divided into three parts. First, on the basis that the target of infrared images dominates the background in the frequency domain, this paper presents a method of candidate region detection. The detection algorithm first generates a saliency map using the discrete cosine transform and then identifies candidate regions by computing and comparing saliency scores of different regions. Second, to extract the features of each candidate region for recognition, the paper presents a local descriptor and subsequently uses locality-constrained linear coding and a pooling operator to obtain the feature vector of the target, and then further completes target recognition via a simple linear classifier. Finally, as preliminary research on the engineering application of related algorithms, the detection and recognition algorithms are transplanted to an embedded platform. The paper conducts experiments on six test sequences to evaluate the performance of the proposed algorithms and the computing efficiency on the embedded platform. An evaluation experiment and comparison experiment verify the effectiveness and practicability of the proposed algorithms.

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          Most cited references16

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          Saliency Detection: A Spectral Residual Approach

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            Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning

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              Salient Object Detection: A Benchmark

              We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted three years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for the state-of-the-art models, provide useful hints toward constructing more challenging large-scale data sets and better saliency models. Finally, we propose probable solutions for tackling several open problems, such as evaluation scores and data set bias, which also suggest future research directions in the rapidly growing field of salient object detection.
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                Author and article information

                Journal
                jais
                Journal of Aerospace Information Systems
                J. Aerosp. Inf. Sys.
                American Institute of Aeronautics and Astronautics
                2327-3097
                31 January 2019
                March 2019
                : 16
                : 3
                : 94-106
                Affiliations
                Nanjing University of Aeronautics and Astronautics , 210016 Nanjing, People’s Republic of China
                University of Southern California , Los Angeles, California 90089
                Author notes
                [*]

                Associate Professor, College of Civil Aviation, Jiangsu; nuaa_dm@ 123456nuaa.edu.cn .

                [†]

                Graduate Student, College of Civil Aviation, Jiangsu.

                [‡]

                Assistant Professor, Jin Cheng College, Jiangsu.

                [§]

                Professor, College of Astronautics, Jiangsu.

                [¶]

                Viterbi School of Engineering, 649 W. 34th Street.

                Article
                I010655 I010655
                10.2514/1.I010655
                90453aaa-2cf6-4cfa-b30a-d772af5f5147
                Copyright © 2019 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the ISSN 2327-3097 (online) to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp.
                History
                : 12 May 2018
                : 7 January 2019
                : 21 January 2019
                Page count
                Figures: 7, Tables: 6
                Funding
                Funded by: National Natural Science Foundation of China10.13039/501100001809
                Award ID: No.61673211
                Award ID: No.U1633105
                Award ID: No.61203170
                Funded by: Aeronautical Science Foundation of China
                Award ID: No.20155152041
                Categories
                Full-Length Papers

                Engineering,Physics,Mechanical engineering,Space Physics
                Engineering, Physics, Mechanical engineering, Space Physics

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