Robust method for infrared small-target detection based on Boolean map visual theory

Qi, Shengxiang; Ming, Delie; Ma, Jie; Sun, Xiaoyang; Tian, Jinwen

doi:10.1364/ao.53.003929

Cited by 43 publications

(21 citation statements)

References 34 publications

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“…The ROC (Receiving Operating Characteristic) curve [29] is an effective tool to describe the quality of detection methods. For an ROC curve, the abscissa and ordinate stand for the probability of false alarm rate (Pf) and detection rate (Pd), which are expressed as Equations (25) and (26), respectively:…”

Section: Results Of Inter-frame Detectionmentioning

confidence: 99%

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

Wan

Ren

et al. 2017

Applied Sciences

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Abstract:In order to detect both bright and dark small moving targets effectively in infrared (IR) video sequences, a saliency histogram and geometrical invariability based method is presented in this paper. First, a saliency map that roughly highlights the salient regions of the original image is obtained by tuning its amplitude spectrum in the frequency domain. Then, a saliency histogram is constructed by means of averaging the accumulated saliency value of each gray level in the map, through which bins corresponding to bright target and dark target are assigned with large values in the histogram. Next, single-frame detection of candidate targets is accomplished by a binarized segmentation using an adaptive threshold, and their centroid coordinates with sub-pixel accuracy are calculated through a connected components labeling method as well as a gray-weighted criterion. Finally, considering the motion characteristics in consecutive frames, an inter-frame false alarm suppression method based on geometrical invariability is developed to improve the precision rate further. Quantitative analyses demonstrate the detecting precision of this proposed approach can be up to 97% and Receiver Operating Characteristic (ROC) curves further verify our method outperforms other state-of-the-arts methods in both detection rate and false alarm rate.

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Section: Results Of Inter-frame Detectionmentioning

confidence: 99%

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

Wan

Ren

et al. 2017

Applied Sciences

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“…Zhao et al [9] proposed another method using a filter to fuse the results of several filters with different directions. Other methods [10][11][12][13][14][15] were based on the contrast mechanism of the human vision system (HVS). Qi et al [10] were inspired by the attention mechanism to produce a color and direction-based Boolean map to fuse, and Chen et al [11] proposed a method of obtaining a local contrast map using a new local contrast measure that measures the degree of difference between the current location and neighbors.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Gray-Temperature Fusion-Based Deep Learning Architecture for Far Infrared Small Target Detection

Ryu

Kim

2019

Journal of Sensors

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This paper proposes the end-to-end detection of a deep network for far infrared small target detection. The problem of detecting small targets has been a subject of research for decades and has been applied mainly in the field of surveillance. Traditional methods focus on filter design for each environment, and several steps are needed to obtain the final detection result. Most of them work well in a given environment but are vulnerable to severe clutter or environmental changes. This paper proposes a novel deep learning-based far infrared small target detection method and a heterogeneous data fusion method to solve the lack of semantic information due to the small target size. Heterogeneous data consists of radiometric temperature data (14-bit) and gray scale data (8-bit), which includes the physical meaning of the target, and compares the effects of the normalization method to fuse heterogeneous data. Experiments were conducted using an infrared small target dataset built directly on the cloud backgrounds. The experimental results showed that there is a significant difference in performance according to the various fusion methods and normalization methods, and the proposed detector showed approximately 20% improvement in average precision (AP) compared to the baseline constant false alarm rate (CFAR) detector.

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“…3(e)], and ground scene with a target [ Fig. 3(f) The proposed DSVoGT small target detector is compared to top-hat filter-based method [1], maxmean filter-based method [2], and max-median filter-based method [2] since these methods are wellstudied and are usually used as baseline methods for assessing new methods [22,32]. Min-local-Laplacian of Gaussian (MLLoG) [33] and LS-SVM [15] methods are also chosen as the baseline methods.…”

Section: A Experiments With Real Imagesmentioning

confidence: 99%

Directional support value of Gaussian transformation for infrared small target detection

Yang

et al. 2015

Appl. Opt.

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Robust small target detection is one of the key techniques in IR search and tracking systems for self-defense or attacks. In this paper we present a robust solution for small target detection in a single IR image. The key ideas of the proposed method are to use the directional support value of Gaussian transform (DSVoGT) to enhance the targets, and use the multiscale representation provided by DSVoGT to reduce the false alarm rate. The original image is decomposed into sub-bands in different orientations by convolving the image with the directional support value filters, which are deduced from the weighted mapped least-squares-support vector machines (LS-SVMs). Based on the sub-band images, a support value of Gaussian matrix is constructed, and the trace of this matrix is then defined as the target measure. The corresponding multiscale correlations of the target measures are computed for enhancing target signal while suppressing the background clutter. We demonstrate the advantages of the proposed method on real IR images and compare the results against those obtained from standard detection approaches, including the top-hat filter, max-mean filter, max-median filter, min-local-Laplacian of Gaussian (LoG) filter, as well as LS-SVM. The experimental results on various cluttered background images show that the proposed method outperforms other detectors.

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Robust method for infrared small-target detection based on Boolean map visual theory

Cited by 43 publications

References 34 publications

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

Heterogeneous Gray-Temperature Fusion-Based Deep Learning Architecture for Far Infrared Small Target Detection

Directional support value of Gaussian transformation for infrared small target detection

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