Dim and small target detection based on feature mapping neural networks

Gao, Zhisheng; Dai, Jiao; Xie, Chunzhi

doi:10.1016/j.jvcir.2019.05.013

Cited by 44 publications

(22 citation statements)

References 20 publications

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“…Jiang et al converted visible maritime images into infrared maritime images as training data and realized infrared maritime target detection based on LSVM and C-NET [36]. Gao et al constructed a neural network FMDNN based on matching of lowdimensional feature and high-dimensional feature for infrared target detection [37]. However, if an infrared small target is too small or appears in a low contrast image, its feature is not obvious for CNN on feature learning [26] [38].…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Maritime Target Detection Based on Integrated Target Saliency Measure

Yang

Dong

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Robust and effective detection of small target in infrared maritime image is a key technology of maritime target search and tracking applications. Infrared small target detection is a challenging task due to the factors such as dim small targets and various complex backgrounds caused by sun glitters and strong waves. In this paper, the integrated target saliency measure (ITSM) based on local and nonlocal spatial information is proposed to improve target detection performance. We combine local heterogeneity property of targets and nonlocal self-correlation property of background with targets' sparsity to separate real targets from background clutters. Firstly, local heterogeneity calculation based on cross-window standard deviation (CSD) is proposed to extract candidate targets preliminarily, which enhances the local intensity difference between small targets and neighboring background. Meanwhile, low-rank representation (LRR) is applied to background prediction and removal, which is followed by adaptive threshold segmentation to enhance target saliency. Finally, we integrate the results obtained from the two steps above to further enhance targets and suppress background clutters. Then, real targets are extracted by an iterative threshold on the integrated map so as to generate the seed map, in addition, the target expansion strategy is exploited to keep full target areas. Experimental results on six data sets show that the proposed method outperforms the state-of-the-art methods in terms of robustness and target detection accuracy.

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Section: Introductionmentioning

confidence: 99%

Infrared Small Maritime Target Detection Based on Integrated Target Saliency Measure

Yang

Dong

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Infrared imaging technology is widely used in aerospace, maritime rescue, and military target detection [1][2][3]. However, the contrast of infrared image is low and the texture details are fuzzy.…”

Section: Introductionmentioning

confidence: 99%

“…It can be seen that the enhanced infrared images have higher contrast, clearer texture detail and more comfortable visual effect. Thus, image enhancement has attracted more and more attention from researchers [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

Liu

Pan

2020

IEEE Access

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In order to solve the problem of low contrast and fuzzy detail in infrared image, we propose an infrared image enhancement method based on multi-scale and adaptive bi-interval histogram equalization with details. The whole image enhancement method mainly consists of four parts: details enhancement, contrast stretch, edge enhancement and reconstruction of enhancement images. Firstly, the multi-scale convolution is used to enhance the details of image; Secondly, taking maximize the variance between classes and minimize the variance as fitness function and solved the threshold of the infrared image by genetic algorithm, then dividing the infrared image into two sub-intervals according to the threshold. After that, the bi-interval histogram equalization with details is applied to enhance the global contrast, at the same time, using the mean square deviation and average gray equalization to improve the brightness of the image. Finally, the enhanced image by adaptive bi-interval histogram equalization with details and the image processed by adaptive limited Laplace operator are fused by linear weighting to reconstruct the final enhancement image. The experimental results show that the proposed method can outperform state-of-the-art ones in both qualitative and quantitative comparisons.

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“…Such algorithms are effective at background modeling for slow-moving scenes but perform poorly when applied to dynamic scenes with large spans. Machine learning-based detection algorithms mainly include detection algorithms based on visual saliency [7,8], detection algorithms based on low-rank and sparse representation [9][10][11][12][13][14][15], and detection algorithms based on convolutional neural networks [16,17]. ese algorithms are able to perform well only in certain scenes.…”

Section: Introductionmentioning

confidence: 99%

Dim and Small Targets Detection in Sequence Images Based on Spatiotemporal Motion Characteristics

Fan

Guo

et al. 2020

Mathematical Problems in Engineering

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In order to effectively enhance the low detection rates of dim and small targets caused by dynamic backgrounds, this paper proposes a detection algorithm for dim and small targets in sequence images based on spatiotemporal motion characteristics. With regard to the spatial domain, this paper proposes an improved anisotropic background filtering algorithm that makes full use of the gradient differences between the target and the background pixels in the eight directions of the spatial domain and selects the mean value of the three directions with the lowest diffusion function in the eight directions as the differential filter to obtain a differential image. Then, the paper proposes a directional energy correlation enhancement algorithm in the time domain. Finally, based on the above preprocessing operations, we construct a dim and small targets detection algorithm in sequence images with local motion characteristics, which achieves target detection by determining the number of occurrences of the target, the number of displacements, and the total cumulative area in these sequential images. Experiments show that the proposed detection algorithm in this paper can effectively improve the detection of dim and small targets in dynamic scenes.

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Dim and small target detection based on feature mapping neural networks

Cited by 44 publications

References 20 publications

Infrared Small Maritime Target Detection Based on Integrated Target Saliency Measure

Infrared Small Maritime Target Detection Based on Integrated Target Saliency Measure

An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

Dim and Small Targets Detection in Sequence Images Based on Spatiotemporal Motion Characteristics

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