Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (2): 151-160.doi: 10.11896/jsjkx.221200045

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Infrared Small Target Detection Based on Dilated Convolutional Conditional GenerativeAdversarial Networks

ZHANG Guodong1, CHEN Zhihua1, SHENG Bin2   

  1. 1 School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
    2 School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China
  • Received:2022-12-07 Revised:2023-03-24 Online:2024-02-15 Published:2024-02-22
  • About author:ZHANG Guodong,born in 1997,postgraduate,is a member of CCF(No.E2434G).His main research interests include computer vision and small target detection.CHEN Zhihua,born in 1969,Ph.D,professor,Ph.Dsupervisor,is a member of CCF(No.12441D).His main research interests include computer vision,machine learning,object detection and image video processing.
  • Supported by:
    National Natural Science Foundation of China(62272164)and Science and Technology on Space Intelligent Control Laboratory(HTKJ2022KL502010).

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Abstract: Deep-learning based object detection methods have achieved great performance in general object detection tasks by virtue of their powerful modeling capabilities.However,the design of deeper network and the abuse of pooling operations also lead to semantic information loss which suppress their performance when detecting infrared small targets with low signal-noise-ratio and small pixel essential features.This paper proposes a novel infrared small target detection algorithm based on dilated convolution conditional generative adversarial network.A dilated convolution stacked generative network makes full use of context information to establish layer-to-layer correlations and facilitate semantic information retainment of infrared small targets in the deep network.In addition,the generative network integrates the channel-space-mixed attention module which selectively amplifies target information and suppresses background clusters.Furthermore,a self-attention association module is proposed to deal with semantic conflict generated during the fusion process between layers.A variety of evaluation metrics are used to compare the proposed method with other state-of-the-arts at present to demonstrate the superiority of the proposed method in complex backgrounds.On the public SIRST dataset,the F score of the proposed model is 64.70% which is 8.29% higher than the traditional method and 7.29% higher than the deep learning method.On the public ISOS dataset,the F score is 64.54%,which is 23.59% higher than the traditional method and 6.58% higher than the deep learning method.

Key words: Infrared small target detection, Conditional generative adversarial network, Feature fusion, Attention mechanism, Dilated convolution

CLC Number: 

  • TP391
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