&lt;title&gt;Adaptive spatial filtering techniques for the detection of targets in infrared imaging seekers&lt;/title&gt;

Morin, Andre

doi:10.1117/12.391662

Cited by 12 publications

(5 citation statements)

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“…So some adaptive and linear/nonlinear methods based on two-order stationary random signal analysis [9][10], such as the least-mean-square (LMS) filtering method has been steadily improving and can effectively suppress the clutter background. However, they failed to work robustly during applications involving changing backgrounds that are frequently encountered.…”

Section: Background Suppression Techniquementioning

confidence: 99%

EMD Based Infrared Image Target Detection Method

Liu

2009

J Infrared Milli Terahz Waves

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Under the complicated background of infrared image, the small target detection is a vital challenging task in modern military. In order to solve this problem, a novel method based on the empirical mode decomposition (EMD) is proposed in the paper, to detect small targets under complicated sea-sky background. The detection process contains two steps: the first step is to suppress the sea-sky background of the infrared image based on EMD; the second step is to segment the target from the background suppressed image through a threshold. The application of infrared images has shown that the performance of the algorithm can detect infrared small target under sea-sky background exactly. Compared with wavelet transformation, the testing results based on EMD method achieve tantamount results wavelet transformation, and even better in some respects. The simulations show that EMD method presented in this paper appears instructive for both theoretical and practical points of view.

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Section: Background Suppression Techniquementioning

confidence: 99%

EMD Based Infrared Image Target Detection Method

Liu

2009

J Infrared Milli Terahz Waves

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“…This filtering serves to facilitate the image segmentation and subsequent target detection by reducing the noise and attenuating the background clutter. The relative efficiency of various filters has been examined in Morin [53]. 2.…”

Section: The Block In the Upper Left-hand Corner Indicates Thatmentioning

confidence: 99%

Infrared target-flare discrimination using a ZISC hardware neural network

Labonté

Deck

2009

J Real-Time Image Proc

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The last generation of infrared imaging aircraft seekers and trackers uses pattern recognition algorithms to find and keep a lock on an aircraft in the presence of decoy flares. These algorithms identify targets, based on the features of the various objects in the missile's field of view. Because modern both aircrafts and missiles fly faster than sound, speed of operation of the target identifier is critical. In this article, we propose a target recognition system that respects this time constraint. It is based on an artificial neural network implemented in hardware, as a set of parallel processors on a commercially available silicon chip called a ZISC, for zero instruction set computer. This chip would be integrated in the infrared missile seeker and tracker. We describe the characteristics of the images that the image processing module of this seeker and tracker extracts from the infrared video frames and show how to construct from these translation and rotation invariant features that can be used as input to the neural network. We determine the individual discriminating power of these features by constructing their histograms, which allows us to eliminate some as not being useful for our purpose. Finally, by testing our system on real data, we show that it has a 90% success rate in aircraft-flare identification, and a processing time that during this time, the aircrafts and missiles will have traveled only a few millimeters. Most of the images on which the neural network makes its mistakes are seen to be hard to recognize even by a human expert.

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“…However, infrared images also have the characteristics of high noise and poor spatial resolution, which presents higher requirements for long-distance target detection, especially in ground background 6 . Traditional methods use the gray feature method 7,8 , the spatial filtering method 9,10 , the Markov random field method [11][12][13] , and the wavelet transform method [14][15][16][17] . However, these methods have some problems, such as requiring prior conditions, artificially defining features, high false alarm rate [18][19][20][21][22] , and inability to suppress the background to infrared targets.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 infrared tank target detection method under ground background

Liang

Yan

Ren

et al. 2023

Preprint

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To solve the problem of low target detection accuracy caused by the change of imaging scale, complex ground background and inconspicuous infrared target characteristics when infrared image seeker detects ground tank targets. In this paper, a You Only Look Once, Transform Head Squeeze-and-Excitation (YOLOv5s-THSE) model is proposed based on the YOLOv5s model, and a multihead attention mechanism is added to the backbone and neck of the network. The Cross Stage Partial, Squeeze-and-Exclusion (CSP_SE) module is added to the neck of the network, a small target detector is introduced into the head of the network, and the complete center section over union loss function is used in the model. Through various improvement measures, the background of the infrared target is suppressed, and the detection ability of the infrared tank target is improved. Experiments on infrared tank target data sets show that the model proposed in this paper can effectively improve the detection performance of infrared tank targets under ground background compared with several methods, such as YOLOv5s, YOLOv5s + SE, and YOLOV 5s + Convective Block Attention Module (CBAM).

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<title>Adaptive spatial filtering techniques for the detection of targets in infrared imaging seekers</title>

Cited by 12 publications

References 0 publications

EMD Based Infrared Image Target Detection Method

EMD Based Infrared Image Target Detection Method

Infrared target-flare discrimination using a ZISC hardware neural network

Improved YOLOv5 infrared tank target detection method under ground background

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