A Method for Weak Target Detection Based on Human Visual Contrast Mechanism

Lv, Ping-Yue; Sun, Songlin; Lin, Cai; Liu, Gaorui

doi:10.1109/lgrs.2018.2866154

Cited by 39 publications

(20 citation statements)

References 26 publications

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“…The size of each single frame image is 480 × 640. To further show the robustness and effectiveness of the LCME approach, we implement seven state‐of‐the‐art infrared small target detection methods for comparison, which are the NLCM [18], RLCM [19], NSM [24], CF [25], TDGS [26], GSS‐ELCM [28], and MF [29]. All the parameters of the compared algorithms are specially set for each tested infrared image so that they show the best performance under the complex background of the test image.…”

Section: Resultsmentioning

confidence: 99%

Local contrast measure with iterative error for infrared small target detection

Yan

Xin

Zhang

2020

IET Image Processing

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Local contrast measure (LCM) has been proved to be an effective method for infrared small target detection. However, the detection performance of LCM decreases dramatically when the background contains strong edges and pixelsized noises with high brightness (PNHB). Based on the analysis of the inherent causes of the poor performance of LCM in extremely complex backgrounds, this study presents an effective LCM with an iterative error. The contribution is as follows: first, the two-dimensional least mean square (TDLMS) filter with an adaptive parameter is applied to suppress the background clutters roughly in each multiscale window. Then, the partial maximum pixel mean is applied to the LCM to optimise the subblock statistical parameters, which achieves excellent strong edges suppression performance. Finally, the iteration error generated by TDLMS and the sub-block weight matrix is updated alternately to further optimise the statistical parameters of the contrast measure to make it more effective in suppressing PNHB. Experimental results demonstrate that the proposed approach is not only superior to the contrast methods in terms of high detection efficiency and low false alarm rate but also has satisfactory adaptability under extremely complex backgrounds.

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Section: Resultsmentioning

confidence: 99%

Local contrast measure with iterative error for infrared small target detection

Yan

Xin

Zhang

2020

IET Image Processing

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“…The target detection performance is quantified by the commonly used receiver operating characteristic (ROC) curve generated from the detection rate (DR) and false alarm rate (FAR) [ 47 ] quantities calculated as:

where

defines the true detections,

defines the actual total targets,

defines the number of false positive detections (incorrectly segmented pixels) and N denotes the number of total pixels in the input image. Detections within a radius of five pixels of the ground truth’s center were considered correct.…”

Section: Performance Evaluationmentioning

confidence: 99%

Detecting Small Size and Minimal Thermal Signature Targets in Infrared Imagery Using Biologically Inspired Vision

Uzair

Brinkworth

Finn

2021

Sensors

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Thermal infrared imaging provides an effective sensing modality for detecting small moving objects at long range. Typical challenges that limit the efficiency and robustness of the detection performance include sensor noise, minimal target contrast and cluttered backgrounds. These issues become more challenging when the targets are of small physical size and present minimal thermal signatures. In this paper, we experimentally show that a four-stage biologically inspired vision (BIV) model of the flying insect visual system have an excellent ability to overcome these challenges simultaneously. The early two stages of the model suppress spatio-temporal clutter and enhance spatial target contrast while compressing the signal in a computationally manageable bandwidth. The later two stages provide target motion enhancement and sub-pixel motion detection capabilities. To show the superiority of the BIV target detector over existing traditional detection methods, we perform extensive experiments and performance comparisons using high bit-depth, real-world infrared image sequences of small size and minimal thermal signature targets at long ranges. Our results show that the BIV target detector significantly outperformed 10 conventional spatial-only and spatiotemporal methods for infrared small target detection. The BIV target detector resulted in over 25 dB improvement in the median signal-to-clutter-ratio over the raw input and achieved 43% better detection rate than the best performing existing method.

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“…For example, a fast target detection method guided by visual saliency (TDGS) [28] extracts visual salient regions by combining small space-scale, high-frequency, high local contrast and isolation distribution, thereby rapidly and accurately detecting dim and small targets. In the case of unknown prior information, the neighborhood saliency map (NSM) [29] is proposed to detect space weak targets in low SCR environment. Coarse-to-fine (CF) [30] framework integrates the advantages of local and nonlocal prior to gradually differentiate small targets from structured edges and unstructured clutters.…”

Section: ) Target Saliency Detectionmentioning

confidence: 99%

“…To verify the advantage of the proposed approach, nine state-of-the-art algorithms are executed for comparisons in this paper, including NSM [29], WNNM-MC [62], RLCM [26], TDGS [28], CF [30], IPI [37], NIPPS [38], SRWS [40], NWIE [36]. All competing algorithms are executed under fair conditions, and their detailed parameter settings are shown in Table IV.…”

Section: B Baselinesmentioning

confidence: 99%

Robust Infrared Superpixel Image Separation Model for Small Target Detection

Yan¹,

Xin²,

Liu³

et al. 2021

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

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Accurate and rapid detection of small targets against complex background is a fundamental requirement of various computer vision systems. This work is the first attempt to apply the superpixel segmentation technology to the field of low resolution infrared small target detection in the extremely complex backgrounds. The main contributions are as follows. First of all, the simple linear iterative cluster (SLIC) algorithm is utilized to accurately classify the raw infrared image into three components: outlier superpixels, stable background superpixels and target superpixels, which appropriately aggregate similar background components as the basic unit of subsequent processing. In SLIC, an optional range of superpixels numbers is specified to robustly implement superpixel segmentation strategy on low resolution infrared images. Second, an outlier superpixel masking (OSM) model is proposed to perform accurate identification and masking of the outlier superpixels with highly heterogeneous backgrounds, thus minimizing false alarm rate. Specially, a 2D Gaussian filter matching the target distribution is introduced to blur the remaining boundary and pixel-sized noises with high brightness (PNHB) while maximizing the signal-to-noise ratio (SNR). Finally, a singular value truncation strategy with entropy weighted sparse factor (SVT-EW) is proposed to implement the final target extraction, which assigns specific sparsity weights for small infrared targets. SVT-EW effectively resolves the background residuals in gray-based threshold segmentation, and therefore generates precise target detection results. Extensive experimental results on fourteen extremely complex infrared natural scenes validate the superiority of the proposed method over the state-of-the-arts with respect to robustness and real-time performance.

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A Method for Weak Target Detection Based on Human Visual Contrast Mechanism

Cited by 39 publications

References 26 publications

Local contrast measure with iterative error for infrared small target detection

Local contrast measure with iterative error for infrared small target detection

Detecting Small Size and Minimal Thermal Signature Targets in Infrared Imagery Using Biologically Inspired Vision

Robust Infrared Superpixel Image Separation Model for Small Target Detection

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