A Local Contrast Method for Small Infrared Target Detection

A robust and effective small dim object detection algorithm is the key to the success of an infrared tracking system. To help solve practical tracking problems, a detecting algorithm based on local contrast method (LCM) and Lucas-Kanade method (LK) is put forward. Firstly, the local contrast map of the input image is obtained using the local contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. Secondly, an adaptive threshold is applied to extract the suspected object regions. Finally, the central points of obtained regions are used as characteristic points, then LK optical flow algorithm to calculate optical flow at these points, and through the instantaneous velocity calculation and selection targets are detected. The experimental result shows that this method works perfectly and can effectively detect infrared targets under complex backgrounds.

Effective detection of small targets plays a pivotal role in infrared (IR) search and track applications for modern military defense or attack. Consequently, an effective small IR target detection algorithm based on a novel local contrast measure (NLCM) is proposed in this letter. Initially, difference of Gaussian band-pass filter is employed to enhance target and suppress background clutter. Then, a segmentation operation is implemented to obtain IR local regions of fixed size larger than general IR small target size. Finally, the salient map is obtained using the NLCM, and an adaptive threshold is applied to extract the target region. Experimental results on two real sequences show that the proposed method has better detection performance compared with conventional baseline methods.

Infrared (IR) small target detection is challenging because the IR imaging lacks detailed features, weak shape features, and a low signal-to-noise ratio (SNR). The existing small IR target detection methods usually focus on improving their high detective performance without considering the execution time. However, high-speed detection is vital for various applications, such as early warning systems, military surveillance, infrared search and track (IRST), etc. This paper proposes a fast and robust single-frame IR small target detection algorithm with a low computational cost while maintaining excellent detection performance. We propose a layered gradient kernel (LGK) based on the contrast properties of the human visual system (HVS) and model it through a three-layer patch image model. The layered gradient kernel is used to convolute with the input IR frame to obtain its gradient map. The target detection is further performed on the acquired gradient map with an adaptive threshold method. This method is compared with eight representative small target detection algorithms to evaluate the performance. Experimental results demonstrate that the algorithm is fast and suitable for real-time applications, and it is very effective even when the small target size is as small as 2×2.

Robust and effective small-target detection in infrared (IR) imagery has significant implications for modern military operations. For the objective of achieving simultaneous solid performance in detection rate, false alarm rate (FAR), and speed, this research provides a novel method making use of local contrast measure (LCM) based on high-boost filters [novel high-boost filter improved LCM (NHBF-ILCM)]. First, a novel high-boost filter is used for target signal enhancement and background clutter suppression, followed by an ILCM used to achieve high-speed local contrast and further target resolution enhancement and clutter suppression. Then, an adaptive threshold is used to segment the target. Finally, through experiments using three sequences with different and typically complex backgrounds, the detection capabilities of the proposed method are confirmed, and these results reveal that the proposed algorithm is able to achieve the best performance and highest efficiency for real IR small-target detection applications among similar algorithms.

Double layer local contrast measure and multi-directional gradient comparison for small infrared target detection

In infrared search and tracking (IRST) systems, small target detection is challenging because IR imaging lacks feature information and has a low signal-to-noise ratio. The recently studied small IR target detection methods have achieved high detection performance without considering execution time. We propose a fast and robust single-frame IR small target detection algorithm while maintaining excellent detection performance. The augmented infrared intensity map based on the standard deviation speeds up small target detection and improves detection accuracy. Density-based clustering helps to detect the shape of objects and makes it easy to identify centroid points. By incorporating these two approaches, the proposed method has a novel approach to the small target detection algorithm. We have self-built 300 images with various scenes and experimented with comparing other methods. Experimental results demonstrate that the proposed method is suitable for real-time detection and effective even when the target size is as small as 2 pixels.

The performance of small target detection restricts the development of the infrared search and track (IRST) system. Against the complicated background clutter of the infrared (IR) image, the small targets are difficult to separate from a noisy background. Aiming at solving the problem of residual background clutter in the local contrast method, a weighted three-layer window local contrast method (WTLLCM) is proposed in this letter. First, the images are filtered by a layered gradient kernel to enhance the contrast between targets and background. Then, a three-layer window is utilized to calculate the local contrast of the filtered images. Next, it is worth mentioning that a simple target aggregation strategy is considered to preserve the integrity of the target. Especially, a new region intensity level (NRIL) algorithm is proposed to weigh the local contrast map to further suppress the background. Finally, the targets are detected by adaptive threshold segmentation. Compared with state-of-the-art small targets detection baseline algorithms based on local contrast, extensive experimental results demonstrate the superiority of the proposed method, especially in complex backgrounds. And instead of utilizing multi-scale windows, multi-scale targets detection is accomplished by using a single-scale window to reduce the calculation of the method.

Small target detection is an arduous mission in infrared search and tracking (IRST) system, especially when the target signal is disturbed by high-intensity background clutters. In view of this situation, this paper presents a robust target detection algorithm based on gradient-intensity joint saliency measure (GISM) to gradually eliminate complex background clutter. Because of thermal remote sensing imaging, the infrared target usually occupies a small area that accords with the optics point spread function (PSF), so it can be distinguished from the background clutter in both gradient and intensity properties. According to this, firstly, the original image is transformed into a gradient map, and the gradient saliency measure (GSM) is calculated to highlight the target signal and suppress the sharp edge clutter, so the candidate targets can be reliably extracted by using the maximum entropy principle. Secondly, the local intensity saliency measure (LISM) is obtained by calculating the gray difference between each candidate region and its local surroundings, so as to preserve the real target and remove intense structural clutter such as black holes or corners. Finally, by fully integrating the gradient and intensity properties, the GISM defined by LISM weighted GSM map can efficiently identify the real target signal and eliminate false alarms. Experimental results prove that the proposed method not only has advantages in background clutter suppression and small target enhancement, but also has reasonable time consumption.

Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false alarm rate, an infrared small target detection algorithm based on saliency detection and support vector machine was proposed. Firstly, we detect salient regions that may contain targets with phase spectrum Fourier transform (PFT) approach. Then, target recognition was performed in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications.

In order to obtain higher small target detection accuracy, this paper presents an effective algorithm inspired by the local contrast mechanism. The proposed method can enhance target signal and suppress background clutter simultaneously. In the first stage, a enhanced image is obtained using the proposed Weighted Laplacian of Gaussian. In the second stage, an adaptive threshold is adopted to segment the target. Experimental results on two challenging image sequences show that the proposed method can detect the bright and dark targets simultaneously, and is not sensitive to sea–sky line of the infrared (IR) image. So it is fit for small IR target detection.

Fast and robust small infrared target detection using absolute directional mean difference algorithm

Target detection is a critical task in interpreting hyperspectral remote sensing images. Small target (such as airplanes) detection is challenging, especially in large-scale complex scenes with high spectral variability of different land cover types. In this paper, we propose a transfer learning-based, large-scale, robust hyperspectral target detector (TLH <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TD) to improve the accuracy of hyperspectral target detection (HTD) in large-scale complex scenes. TLH <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TD learns the spatial features of hyperspectral targets from high-resolution remote sensing images and achieves high-precision HTD with fused spatial-spectral features. It comprises three parts: (1) The coupled target-background sample expansion (CTBSE) module is designed to expand the labeled hyperspectral target and background samples with sufficient high-resolution, labeled RGB images and a few labeled hyperspectral samples. (2) The hard positive and negative example mining (HPNEM) module trains the hard positive and negative samples to enhance the discriminative ability of the network, addressing the problem of inadequate sample training in large-scale hyperspectral images (HSIs). (3) The spatial-spectral weighted subspace (SSWS) module is designed to fuse the spatial features extracted from the target detection network and the spectral features based on the Mahalanobis distance. The results show that: (1) The TLH <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TD achieves average area under the curve (AUC) values of 0.96, 0.96, and 0.93 on small-sized, medium-sized, and large-sized HSIs, respectively, achieving the highest accuracy compared with other HTD algorithms. (2) The TLH <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TD exhibits the highest detection time efficiency for medium-sized and large-sized HSIs. (3) For large-sized HSIs, when most HTD algorithms fail, TLH <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TD exhibits significantly higher accuracy and time efficiency than other methods. This is an important achievement to meet the robust target detection tasks of large-scale hyperspectral remote sensing images.

The detection of small infrared targets is still a challenging task and efficient and accurate detection plays a key role in modern infrared search and tracking military applications. However, small infrared targets are difficult to detect due to their weak brightness, small size and lack of shape, structure, texture, and other information elements. In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high‐brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high‐brightness clutter. Second, a density‐peaked global search method is used to determine the location of candidate targets in the preprocessed image. We then use local contrast to the candidate target points to enhance the gradient features and suppress background clutter. The Facet model is used to compute multidirectional gradient features at each point. A new efficient surrounding symmetric region partitioning scheme is constructed to capture the gradient characteristics of targets of different sizes in eight directions, followed by weighting the candidate target gradient characteristics using the standard deviation of the symmetric region difference. Finally, an adaptive threshold segmentation method is used to extract small targets. Experimental results show that the method proposed in this paper has better detection accuracy and robustness compared with other detection methods.

The robust small and dim target detection is a key technique for infrared search and track system, and it is still a challenging task due to the complex scenarios and noise. An approach based on total variation regularization and principal component pursuit (TV-PCP) method is proposed to achieve robust target detection performance in non-smooth and non-uniform scene, because total variation (TV) regularization could describe the inner smooth and crisp edges of background. Nevertheless, there are still two drawbacks: 1) TV-PCP model only considers the spatial information of single frame and 2) the vanilla nuclear norm adopted in TV-PCP model is only suitable for the scenarios of sufficient edge samples, which would cause some sparse background residuals and increase the false alarm rate. Inspired by this, a novel small target detection approach based on a new reweighted infrared patch image (IPI) model and TV regularization is proposed by utilizing both spatial and temporal information. Then, the TV regularization and weighted nuclear norm minimization are adopted to separate the target and background. For the low-rank background part, we adopt the weighted nuclear norm and TV regularization to describe the smoothness. For sparse target part and noise part, we use the reweighted l1 norm and Frobenius norm term to characterize, respectively. Finally, the proposed model can be solved efficiently by the Alternating Direction Method of Multipliers (ADMM) method. Extensive experiments demonstrate that the background suppression ability and target detection probability of the proposed method is better than the other competitive methods.

This paper presents a separate spatio-temporal filter based small infrared target detection method to address the sea-based infrared search and track (IRST) problem in dense sun-glint environment. It is critical to detect small infrared targets such as sea-skimming missiles or asymmetric small ships for national defense. On the sea surface, sun-glint clutters degrade the detection performance. Furthermore, if we have to detect true targets using only three images with a low frame rate camera, then the problem is more difficult. We propose a novel three plot correlation filter and statistics based clutter reduction method to achieve robust small target detection rate in dense sun-glint environment. We validate the robust detection performance of the proposed method via real infrared test sequences including synthetic targets.