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Applied Sciences
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Advances in Small Infrared Target Detection Using Deep Learning
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Advances in Small Infrared Target Detection Using Deep Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 5330

Special Issue Editor

Prof. Dr. Sungho Kim

E-Mail Website
Guest Editor
Department of Electronic Engineering, Yeungnam University, Gyeongsan 35841, Korea
Interests: remote object detection; infrared images; hyperspectral images; sensor fusion; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote infrared small target detection is a well-known problem in surveillance areas. Although small infrared targets can be detected successfully in homogeneous sky background, they are still very challenging to detect in cluttered complex backgrounds due to many false alarms.

This Special Issue will publish recent advances in small infrared target detection using deep learning methods. The scope is as follows.

  • Deep learning-based super-resolution of infrared images;
  • Deep learning-based signal-to-noise enhancement;
  • Deep learning-based background clutter suppression;
  • Deep learning-based panoptic segmentation;
  • Deep learning-based small infrared target detection in complex environment;
  • Deep learning-based moving infrared target detection.

This Special Issue deals with various applications of recent deep learning methods, such as convolutional neural network, variational autoencoder, generative adversarial network, self-attention, transformer, recurrent neural network, explainable artificial intelligence, and so on.

Prof. Dr. Sungho Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • infrared
  • small target
  • detection
  • deep learning
  • clutter

Published Papers (2 papers)

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Research

28 pages, 210353 KiB  
Article
ODPA-CNN: One Dimensional Parallel Atrous Convolution Neural Network for Band-Selective Hyperspectral Image Classification
by Byungjin Kang, Inho Park, Changmin Ok and Sungho Kim
Appl. Sci. 2022, 12(1), 174; https://0-doi-org.brum.beds.ac.uk/10.3390/app12010174 - 24 Dec 2021
Cited by 5 | Viewed by 2543
Abstract
Recently, hyperspectral image (HSI) classification using deep learning has been actively studied using 2D and 3D convolution neural networks (CNN). However, they learn spatial information as well as spectral information. These methods can increase the accuracy of classification, but do not only focus [...] Read more.
Recently, hyperspectral image (HSI) classification using deep learning has been actively studied using 2D and 3D convolution neural networks (CNN). However, they learn spatial information as well as spectral information. These methods can increase the accuracy of classification, but do not only focus on the spectral information, which is a big advantage of HSI. In addition, the 1D-CNN, which learns only pure spectral information, has limitations because it uses adjacent spectral information. In this paper, we propose a One Dimensional Parellel Atrous Convolution Neural Network (ODPA-CNN) that learns not only adjacent spectral information for HSI classification, but also spectral information from a certain distance. It extracts features in parallel to account for bands of varying distances. The proposed method excludes spatial information such as the shape of an object and performs HSI classification only with spectral information about the material of the object. Atrous convolution is not a convolution of adjacent spectral information, but a convolution between spectral information separated by a certain distance. We compare the proposed model with various datasets to the other models. We also test with the data we have taken ourselves. Experimental results show a higher performance than some 3D-CNN models and other 1D-CNN methods. In addition, using datasets to which random space is applied, the vulnerabilities of 3D-CNN are identified, and the proposed model is shown to be robust to datasets with little spatial information. Full article
(This article belongs to the Special Issue Advances in Small Infrared Target Detection Using Deep Learning)
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16 pages, 33492 KiB  
Article
Efficient Shot Detector: Lightweight Network Based on Deep Learning Using Feature Pyramid
by Chansoo Park, Sanghun Lee and Hyunho Han
Appl. Sci. 2021, 11(18), 8692; https://0-doi-org.brum.beds.ac.uk/10.3390/app11188692 - 17 Sep 2021
Cited by 5 | Viewed by 1899
Abstract
Convolutional-neural-network (CNN)-based methods are continuously used in various industries with the rapid development of deep learning technologies. However, an inference efficiency problem was reported in applications that require real-time performance, such as a mobile device. It is important to design a lightweight network [...] Read more.
Convolutional-neural-network (CNN)-based methods are continuously used in various industries with the rapid development of deep learning technologies. However, an inference efficiency problem was reported in applications that require real-time performance, such as a mobile device. It is important to design a lightweight network that can be used in general-purpose environments such as mobile environments and GPU environments. In this study, we propose a lightweight network efficient shot detector (ESDet) based on deep training with small parameters. The feature extraction process was performed using depthwise and pointwise convolution to minimize the computational complexity of the proposed network. The subsequent layer was formed in a feature pyramid structure to ensure that the extracted features were robust to multiscale objects. The network was trained by defining a prior box optimized for the data set of each feature scale. We defined an ESDet-baseline with optimal parameters through experiments and expanded it by gradually increasing the input resolution for detection accuracy. ESDet training and evaluation was performed using the PASCAL VOC and MS COCO2017 Dataset. Moreover, the average precision (AP) evaluation index was used for quantitative evaluation of detection performance. Finally, superior detection efficiency was demonstrated through the experiment compared to the conventional detection method. Full article
(This article belongs to the Special Issue Advances in Small Infrared Target Detection Using Deep Learning)
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