MSAFFNet: A Multiscale Label-Supervised Attention Feature Fusion Network for Infrared Small Target Detection

Tong, Xiaozhong; Su, Shaojing; Wu, Peng; Guo, Runze; Wei, Junyu; Zuo, Zhen; Sun, Bin

doi:10.1109/tgrs.2023.3279253

Cited by 21 publications

(5 citation statements)

References 47 publications

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“…The algorithm proposed in this paper can be applied to medical diagnosis, fire security, dangerous gas leakage, and other fields [30]; it can also be applied to military fields, such as target detection and the tracking of drones, missiles, aircraft, etc. [31].…”

Section: Discussionmentioning

confidence: 99%

Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images

Yang

Zou

et al. 2023

Applied Sciences

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To address the issue of poor tracking accuracy and the low recognition rate for multiple small targets in infrared images caused by uneven image intensity, this paper proposes an accurate tracking algorithm based on optical flow estimation. The algorithm consists of several steps. Firstly, an infrared image subspace model is established. Secondly, a full convolutional network (FCN) is utilized for local double-threshold segmentation of the target image. Furthermore, a target observation model is established using SIR filtering particles. Lastly, a shift vector sum algorithm is employed to enhance the intensity of the infrared image at a certain time scale in accordance with the relationship between the pixel intensity and the temporal parameters of the detected image. Experimental results demonstrate that the multi-target tracking accuracy (MOTA) reaches 79.7% and that the inference speed frame per second (FPS) reaches 42.3. Moreover, the number of ID switches during tracking is 9.9% lower than that of the MOT algorithm, indicating high recognition of cluster small targets, stable tracking performance, and suitability for tracking weak small targets on the ground or in the air.

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Section: Discussionmentioning

confidence: 99%

Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images

Yang

Zou

et al. 2023

Applied Sciences

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“…It has been proved that the classical encoder-decoder structure can achieve better results in the semantic segmentation task [34], and some researchers have carried out work on the improvement and innovation of the classical codec structure. Tong et al [26] proposed MSAFFNet, which introduced the EIFAM module containing edge information based on the codec structure and constructed multi-scale labels to focus on the details of target contour and internal features. Wu et al [27] proposed UIU-Net (U-Net in U-Net).…”

Section: Infrared Small Target Detection Methodsmentioning

confidence: 99%

“…Based on the small proportion of small targets in the overall image, some methods [24,25] solve the problem with infrared small target detection by suppressing the background area to make the network pay more attention to the target area. There are also some studies [26][27][28][29] that consider how to improve and innovate based on classic encoding and decoding structures.…”

Section: Introductionmentioning

confidence: 99%

CCRANet: A Two-Stage Local Attention Network for Single-Frame Low-Resolution Infrared Small Target Detection

Wang,

Xiao,

Dou

et al. 2023

Remote Sensing

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Infrared small target detection technology is widely used in infrared search and tracking, infrared precision guidance, low and slow small aircraft detection, and other projects. Its detection ability is very important in terms of finding unknown targets as early as possible, warning in time, and allowing for enough response time for the security system. This paper combines the target characteristics of low-resolution infrared small target images and studies the infrared small target detection method under a complex background based on the attention mechanism. The main contents of this paper are as follows: (1) by sorting through and expanding the existing datasets, we construct a single-frame low-resolution infrared small target (SLR-IRST) dataset and evaluate the existing datasets on three aspects—target number, target category, and target size; (2) to improve the pixel-level metrics of low-resolution infrared small target detection, we propose a small target detection network with two stages and a corresponding method. Regarding the SLR-IRST dataset, the proposed method is superior to the existing methods in terms of pixel-level metrics and target-level metrics and has certain advantages in model processing speed.

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“…Furthermore, following the method adopted in [39], our evaluation indexes include Intersection over Union (IoU), Precision (P), Recall (R), F-measure (F1), and ROC curves, allowing to assess performance across various dimensions.…”

Section: Experimental Settingsmentioning

confidence: 99%

DEAR: A Novel Deep-Level Semantics Feature Reinforce Framework for Infrared Small Object Segmentation

Ni,

Zhao,

et al. 2024

Preprint

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Infrared Small Object Segmentation (ISOS) faces challenges in isolating small and faint objects from infrared images due to their limited texture details and small spatial presence. Existing deep learning methods have shown promise but often assume that these networks can effectively map small objects to deep semantic features. This mapping, however, may not be accurately learned by the model due to the excessive downsampling, which may cause the loss of high-level semantic representations essential for accurately identifying small objects in infrared images. To address this issue, this study introduces a novel learning paradigm, DEAR, designed to reinforce deep-level features of infrared small objects from three perspectives. Specifically, the Multi-Scale Nested (MSN) module merges shallow object features with deep semantic features through iterative interactions. The Central Difference Convolution (CDC) module enhances semantic contrast between small objects and their backgrounds, minimizing information loss during downsampling. Additionally, the Orthogonal Dynamic Fusion (ODF) module boosts the representation of deep object features through self-supervised learning. Experimental evaluations on the NUAA-SIRST and NUDT-SIRST datasets show that DEAR significantly outperforms 16 state-of-the-art methods in ISOS, demonstrating its effectiveness. The code is publicly available online at \href{https://github.com/nieyihe888/DEFER}{https://github.com/nieyihe888/DEAR}.

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MSAFFNet: A Multiscale Label-Supervised Attention Feature Fusion Network for Infrared Small Target Detection

Cited by 21 publications

References 47 publications

Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images

Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images

CCRANet: A Two-Stage Local Attention Network for Single-Frame Low-Resolution Infrared Small Target Detection

DEAR: A Novel Deep-Level Semantics Feature Reinforce Framework for Infrared Small Object Segmentation

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