Infrared Small Target Tracking Algorithm via Segmentation Network and Multistrategy Fusion

Kou, Renke; Wang, Chunping; Yu, Ying; Peng, Zhenming; Huang, Fuyu; Fu, Qiang

doi:10.1109/tgrs.2023.3286836

Cited by 23 publications

(3 citation statements)

References 37 publications

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“…On the other hand, deep learning-based methods automate feature extraction for targets using deep neural networks (DNN). In this context, detection networks regress target bounding boxes to approximate the location of small targets [37,38], while semantic segmentation networks treat small target detection as a semantic segmentation problem [39,40], performing pixel-level segmentation to accurately delineate the target area. The latter approach is becoming increasingly prevalent in research due to its potential for precise small target region identification [41], heralding a promising direction for future advancements in the domain.…”

Section: Introductionmentioning

confidence: 99%

TFCD-Net: Target and False Alarm Collaborative Detection Network for Infrared Imagery

Cao,

Li,

Deng

et al. 2024

Preprint

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Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. The detection of small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches primarily focus on modeling targets while often neglecting false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features on multiple scales and employing stepwise upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across multiple scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets to produce the final detection result. Experiments on public datasets demonstrate that, compared to other methods, our model achieves high accuracy in segmenting targets while being able to extract false alarm sources which can be used for further studies.

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Section: Introductionmentioning

confidence: 99%

TFCD-Net: Target and False Alarm Collaborative Detection Network for Infrared Imagery

Cao,

Li,

Deng

et al. 2024

Preprint

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“…In civilian domains, this forms the bedrock for implementing marine resource regulation, illegal fishing monitoring, and aiding maritime rescue operations [4], while in military domains, it finds utility in patrolling territorial waters, safeguarding maritime rights, and monitoring critical port facilities [5]. In recent years, with the exponential augmentation of computational prowess, deep learning has witnessed accelerated development [6,7]. The application of deep learning-based target detection algorithms in maritime target detection not only economizes on human and material resources but also amplifies efficiency, thereby playing a pivotal role in ensuring maritime security [8].…”

Section: Introductionmentioning

confidence: 99%

“…Remote Sens. 2024, 16, x FOR PEER REVIEW 2 of 21 tional prowess, deep learning has witnessed accelerated development [6,7]. The application of deep learning-based target detection algorithms in maritime target detection not only economizes on human and material resources but also amplifies efficiency, thereby playing a pivotal role in ensuring maritime security [8].…”

Section: Introductionmentioning

confidence: 99%

FCNet: Flexible Convolution Network for Infrared Small Ship Detection

Guo,

Ma,

et al. 2024

Remote Sensing

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The automatic monitoring and detection of maritime targets hold paramount significance in safeguarding national sovereignty, ensuring maritime rights, and advancing national development. Among the principal means of maritime surveillance, infrared (IR) small ship detection technology stands out. However, due to their minimal pixel occupancy and lack of discernible color and texture information, IR small ships have persistently posed a formidable challenge in the realm of target detection. Additionally, the intricate maritime backgrounds often exacerbate the issue by inducing high false alarm rates. In an effort to surmount these challenges, this paper proposes a flexible convolutional network (FCNet), integrating dilated convolutions and deformable convolutions to achieve flexible variations in convolutional receptive fields. Firstly, a feature enhancement module (FEM) is devised to enhance input features by fusing standard convolutions with dilated convolutions, thereby obtaining precise feature representations. Subsequently, a context fusion module (CFM) is designed to integrate contextual information during the downsampling process, mitigating information loss. Furthermore, a semantic fusion module (SFM) is crafted to fuse shallow features with deep semantic information during the upsampling process. Additionally, squeeze-and-excitation (SE) blocks are incorporated during upsampling to bolster channel information. Experimental evaluations conducted on two datasets demonstrate that FCNet outperforms other algorithms in the detection of IR small ships on maritime surfaces. Moreover, to propel research in deep learning-based IR small ship detection on maritime surfaces, we introduce the IR small ship dataset (Maritime-SIRST).

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