IR Small Target Detection And Prediction With ANNs Trained Using ASSET

Sinn, Yong U; Hopkinson, Kenneth M.; Borghetti, Brett J.; Steward, Bryan J.

doi:10.1109/aero.2019.8741671

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Due to the small proportion of target area, lack of rich texture information and low target intensity, target detection algorithms in the general field, such as Fast-RCNN, YOLO, SSD, etc. [14,15], are not suitable for such application scenarios. Shi M et al [17] proposes a method for a single frame image using semantic segmentation, better background suppress factor and SNR gain can be obtained by segmenting the image pixel by pixel.…”

Section: Neural-network-basedmentioning

confidence: 99%

Dim and Small Target Detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

Liu

Li²,

et al. 2021

IEEE Access

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Infrared dim and small target detection is widely used in military and civil fields. Traditional methods in that application rely on the local contrast between the target and background for single-frame detection. On the other hand, those algorithms depend on the motion model with fixed parameters for multiframe association. For the great similarity of gray value and the dynamic changes of motion model parameters in the condition of low SNR and strong clutter, those methods possess weak robustness, low detection probability, and high false alarm rate. In this paper, an infrared video sequences encoding and decoding model based on Bidirectional Convolutional Long Short-Term Memory structure (Bi-Conv-LSTM) and 3D Convolutional structure (3D-Conv) is proposed, addressing the problem of high similarity and dynamic changes of parameters. For solving the problem of dynamic change in parameters, Bi-Conv-LSTM structure is used to learn the motion model of targets. And for the problem of low local contrast, 3D-Conv structure is adopted to extend receptive field in the time dimension. In order to improve the precision of detection, the Decoding part is divided into two different full connection with distinctive active function. Simulation results show that the trajectory detection accuracy of the proposed model is more than 90% under the condition of low SNR and maneuvering motion, which is better than traditional method with 80% in DB-TBD 20% in others. Real data experiment illustrate that that our proposed method can detect small infrared targets with a low false alarm rate and high detection probability.

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Section: Neural-network-basedmentioning

confidence: 99%

Dim and Small Target Detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

Liu

Li²,

et al. 2021

IEEE Access

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“…Infrared target detection and tracking is of great significance because of its comprehensive military applications, such as video surveillance, infrared imaging precision guidance, visual monitoring and so on [1].The main challenge in these areas is that small infrared targets are fuzzy in space, and their size in the image is several pixels, which is easy to be confused with part of the noise [2]. For infrared small target detection, our team has created many algorithms, such as [3].…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Tracking Based on Target Spatial Distribution With Improved Kernelized Correlation Filtering

Kou¹,

Hamdulla²

2021

Preprint

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The application of correlation filtering in infrared small target tracking has been a mature research field. Traditionalcorrelation filtering is to describe the target features by using a single feature, which can not solve the problem of target occlusion. Because of the fast moving speed and lack of re-detection mechanism, the target tracking will produce offset, which leads to the performance of the tracker to decline. In view of the above problems, a new multi feature re detection framework is proposed for long-term tracking of small targets. The feature selects multi feature weighting function, considers the importance of intensity feature to infrared target and different regions, calculates the gray distribution weighting function of the target, and combines the weighting function into the correlation filter. Before updating the template, to verify the reliability of target detection, the average peak correlation energy is used as the confidence of candidate region. When the target is completely occluded, the prediction result of Kalman filter is used as the optimal estimation of target position in the next frame. A large number of experimental results on different video sequences show that the tracking accuracy of this method is greatly improved compared with the baseline method.

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“…Fan et al [9] proposed an infrared image enhancement method based on convolutional neural network, which could enhance the infrared target area in the state of background clutters and low contrast [10]. In order to design a deep network with better self-learning ability and self-adaptive ability, the temporal and spatial features of motion imagery were used in artificial neural network (ANN) for infrared small target detection [11]. After the ANN was used in the target detection, many kinds of immune neural networks are used for image target processing.…”

Section: Introductionmentioning

confidence: 99%

Infrared Target Extraction Based on Immune Extension Neural Network

Dong

Hao

et al. 2020

IEEE Access

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This paper addresses the difficulties of target extraction from low contrast low resolution infrared images. The extension neural network is integrated with the negative selection algorithm (NSA) in artificial immunity. Based on the specific recognition principle of long non-coding RNA (lncRNA) in biological immune system, the mature self-extension detectors are calculated to extract infrared target and background. Upon the convergence of clustering process, the output layer of network merges the categories corresponding to the target to be detected. The proposed method, it needs less detectors than the traditional NSA algorithm. Compared with several target detection algorithms, the proposed algorithm has higher detection accuracy and higher time efficiency. INDEX TERMS Artificial immunity, extension analysis, negative selection algorithm (NSA), long noncoding RNA (lncRNA), infrared target extraction.

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IR Small Target Detection And Prediction With ANNs Trained Using ASSET

Cited by 5 publications

References 12 publications

Dim and Small Target Detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

Dim and Small Target Detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

Infrared Small Target Tracking Based on Target Spatial Distribution With Improved Kernelized Correlation Filtering

Infrared Target Extraction Based on Immune Extension Neural Network

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