Local Convergence Index-Based Infrared Small Target Detection against Complex Scenes

Cao, Siying; Deng, Jiakun; Luo, Junhai; Li, Zhi; Hu, Junsong; Peng, Zhenming

doi:10.3390/rs15051464

Cited by 11 publications

(7 citation statements)

References 35 publications

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“…At present, Cao et al [25] have proposed an automatic detection method for infrared small targets under complex backgrounds and conducted extensive experiments on the public data sets NUDT-SIRST and NUAA-SIRST. The results show that the proposed detection method exhibits excellent performance.…”

Section: Related Workmentioning

confidence: 99%

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Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5

Cheng,

Yuan,

Chen

et al. 2023

Applied Sciences

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The images captured using UAVs during inspection often contain a great deal of small targets related to transmission lines. These vulnerable elements are critical for ensuring the safe operation of these lines. However, due to various factors such as the small size of the targets, low resolution, complex background, and potential target aggregation, achieving accurate and real-time detection becomes challenging. To address these issues, this paper proposes a detection algorithm called P2-ECA-EIOU-YOLOv5 (P2E-YOLOv5). Firstly, to tackle the challenges posed by the issues of complex background and environmental interference impacting small targets, an ECA attention module is integrated into the network. The module effectively enhances the network’s focus on small targets, while concurrently mitigating the influence of environmental interference. Secondly, considering the characteristics of small target size and low resolution, a new high-resolution detection head is introduced, making the network more sensitive to small targets. Lastly, the network utilizes the EIOU_Loss as the regression loss function to improve the positioning accuracy of small targets, especially when they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P (precision) of 96.0% and an average accuracy (mAP) of 97.0% for small-target detection in transmission lines.

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Section: Related Workmentioning

confidence: 99%

“…Additionally, to demonstrate the effectiveness of the proposed method in this paper, we compared it with the latest infrared small object detection method [25] through comparative experiments on the same public data sets, NUDT-SIRST and NUAA-SIRST.…”

Section: Comparative Experimentsmentioning

confidence: 99%

Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5

Cheng,

Yuan,

Chen

et al. 2023

Applied Sciences

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“…Single-frame detection methods diverge into two categories: non-deep learning and deep learning approaches. The non-deep learning methods encompass background suppression techniques, which seek to isolate the target region by subtracting an estimated background from the input image [24]; target enhancement methods, which employ calculations of local contrast and saliency to search or amplify the target region [25][26][27][28][29][30]; image structure-based methods, which assume a mathematical model of low-rank background and sparse targets within the infrared image, solving for the target region through optimization techniques [31][32][33][34]; and classifier-based methods, which function by extracting potential target regions and their features, and subsequently classifying those features to identify true targets [35,36]. These methods rely on a priori knowledge to model infrared small targets.…”

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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“…Infrared target detection has the benefits of all-weather, long-range, and strong antiinterference [1], so UAV-based infrared target detection has an important role in military [2], accident search and rescue [3,4], and traffic monitoring [5][6][7]. However, the aerial images captured by UAVs often contain numerous multi-scale, small targets, which typically have limited features available for extraction [8].…”

Section: Introductionmentioning

confidence: 99%

ITD-YOLOv8: An Infrared Target Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles

Zhao,

Zhang,

Zhang

et al. 2024

Drones

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A UAV infrared target detection model ITD-YOLOv8 based on YOLOv8 is proposed to address the issues of model missed and false detections caused by complex ground background and uneven target scale in UAV aerial infrared image target detection, as well as high computational complexity. Firstly, an improved YOLOv8 backbone feature extraction network is designed based on the lightweight network GhostHGNetV2. It can effectively capture target feature information at different scales, improving target detection accuracy in complex environments while remaining lightweight. Secondly, the VoVGSCSP improves model perceptual abilities by referencing global contextual information and multiscale features to enhance neck structure. At the same time, a lightweight convolutional operation called AXConv is introduced to replace the regular convolutional module. Replacing traditional fixed-size convolution kernels with convolution kernels of different sizes effectively reduces the complexity of the model. Then, to further optimize the model and reduce missed and false detections during object detection, the CoordAtt attention mechanism is introduced in the neck of the model to weight the channel dimensions of the feature map, allowing the network to pay more attention to the important feature information, thereby improving the accuracy and robustness of object detection. Finally, the implementation of XIoU as a loss function for boundary boxes enhances the precision of target localization. The experimental findings demonstrate that ITD-YOLOv8, in comparison to YOLOv8n, effectively reduces the rate of missed and false detections for detecting multi-scale small targets in complex backgrounds. Additionally, it achieves a 41.9% reduction in model parameters and a 25.9% decrease in floating-point operations. Moreover, the mean accuracy (mAP) attains an impressive 93.5%, thereby confirming the model’s applicability for infrared target detection on unmanned aerial vehicles (UAVs).

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Local Convergence Index-Based Infrared Small Target Detection against Complex Scenes

Cited by 11 publications

References 35 publications

Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5

Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5

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

ITD-YOLOv8: An Infrared Target Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles

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