HPN-SOE: Infrared Small Target Detection and Identification Algorithm Based on Heterogeneous Parallel Networks with Similarity Object Enhancement

Infrared image small target detection and identification is a massive challenge with vast applications in essential places such as military industries and airports. Homoplastically, the unclear outline of the target in infrared images, and the complex image background, and the tiny percentage of tar...

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Vydáno v:IEEE sensors journal Ročník 23; číslo 12; s. 1
Hlavní autoři: Liu, Shanliang, Wu, Renbiao, Qu, Jingyi, Li, Yunlong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Airports
Algorithms
Channels
Computational modeling
Computer networks
Convolution
Datasets
Deep learning
Feature extraction
Feature maps
Image acquisition
Image enhancement
Infrared imagery
Infrared images
Infrared imaging
Modules
Object detection
parallel networks
Real-time systems
Sensors
Similarity
small target detection
Target acquisition
Target detection
target enhancement
target identification
ISSN:1530-437X, 1558-1748
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Popis
Shrnutí:Infrared image small target detection and identification is a massive challenge with vast applications in essential places such as military industries and airports. Homoplastically, the unclear outline of the target in infrared images, and the complex image background, and the tiny percentage of targets in infrared images make it challenging to accurately acquire the targets' features and other characteristics, all of which make the research of target detection and identification in infrared images significantly challenging. In this paper, we first contribute an open dataset of Single-frame Infrared Small Target dataset dubbed CAUC-SIRST. After that, the similarity object enhancement module is obtained based on the input feature maps by calculating the Wasserstein distance between the local target and the local background. Then a heterogeneous parallel backbone network structure is constructed, and the feature maps obtained from three different backbone network channels are fused and stitched together. As above, the general convolution channel adaptively extracts feature information; the SimAM channel increases the attention of potential targets, and the similarity object enhancement channel increases the weight of targets. Finally, fusing the feature maps of the three different channels makes it possible to increase the weight of potential targets while retaining the original basic information, this method is named HPN-SOE. Embedding the algorithm into the camera can form a sensor for infrared target detection and recognition. With extensive experimental results demonstrate our outstanding performance, which outperforms other existing methods by achieving a detection accuracy of 85.7% and a detection speed of 31.2 fps.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3269085