Infrared Target Recognition Based On Improved Convolution Neural Network

Xu, Laixiang; Liu, Gang; Cao, Bingxu; Zhang, Peigen; Liu, Sen

doi:10.1145/3375998.3376000

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Along with the diversification of shooting scenes, the demand for environment perception such as target recognition and tracking in images is increasing [6][7][8][9][10][11]. However, in indoor scenes shaded with obstacles or outdoor open scenes, the accuracy and speed of existing image processing technologies for target recognition cannot yet meet the actual demand, and the accomplishment of environment perception tasks is far from satisfying [12][13][14][15][16][17]. In reality, many image target recognition software and hardware products have been put into the market.…”

Section: Introductionmentioning

confidence: 99%

Fast Target Recognition Method Based on Multi-Scale Fusion and Deep Learning

Sun¹,

Kuang²,

Zhang³

2022

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Under the natural state of unrestricted conditions, the accuracy of effective recognition of image target information captured by ordinary cameras is significantly reduced. At present, the mainstream research methods for image target recognition focus on the processing of images based on algorithms with strong ability to describe image target features so as to improve the image target recognition performance in cases of complex noise interference. However, most of the methods cannot adapt to various changes in the background when the environment changes. Therefore, this article conducts studies on the fast target recognition method based on multi-scale fusion and deep learning. The optimized local binary pattern algorithm and the HOG algorithm are used to extract the image target features, the dimension reduction of the extracted image target features is carried out based on the generalized discriminant analysis algorithm, and multi-scale fusion of image targets is accomplished based on the discriminant correlation analysis. The experimental results verify the effectiveness of the proposed algorithm.

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