UIU-Net: U-Net in U-Net for Infrared Small Object Detection

Learning-based infrared small object detection methods currently rely heavily on the classification backbone network. This tends to result in tiny object loss and feature distinguishability limitations as the network depth increases. Furthermore, small objects in infrared images are frequently emerg...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 32; p. 1
Main Authors: Wu, Xin, Hong, Danfeng, Chanussot, Jocelyn
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Artificial Intelligence
attention mechanism
Computer networks
Computer Science
Context
Datasets
Decoding
deep learning
deep multi-scale feature
Feature extraction
feature interaction
Image resolution
Infrared imagery
Infrared small object
Integrated circuits
Learning
local and global context information
Maintenance
Modules
Object detection
Object recognition
Semantics
Visualization
Infrared small object
deep learning
feature interaction
attention mechanism
deep multi-scale feature
local and global context information
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:Learning-based infrared small object detection methods currently rely heavily on the classification backbone network. This tends to result in tiny object loss and feature distinguishability limitations as the network depth increases. Furthermore, small objects in infrared images are frequently emerged bright and dark, posing severe demands for obtaining precise object contrast information. For this reason, we in this paper propose a simple and effective "U-Net in U-Net" framework, UIU-Net for short, and detect small objects in infrared images. As the name suggests, UIU-Net embeds a tiny U-Net into a larger U-Net backbone, enabling the multi-level and multi-scale representation learning of objects. Moreover, UIU-Net can be trained from scratch, and the learned features can enhance global and local contrast information effectively. More specifically, the UIU-Net model is divided into two modules: the resolution-maintenance deep supervision (RM-DS) module and the interactive-cross attention (IC-A) module. RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information. Further, IC-A encodes the local context information between the low-level details and high-level semantic features. Extensive experiments conducted on two infrared single-frame image datasets, i.e., SIRST and Synthetic datasets, show the effectiveness and superiority of the proposed UIU-Net in comparison with several state-of-the-art infrared small object detection methods. The proposed UIU-Net also produces powerful generalization performance for video sequence infrared small object datasets, e.g., ATR ground/air video sequence dataset. The codes of this work are available openly at https://github.com/danfenghong/IEEE_TIP_UIU-Net.
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SourceType-Scholarly Journals-1
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2022.3228497