Dim Point Target Detection Research Articles

Dynamic programming network for point target detection

To improve the efficiency of the dim point target detection based on dynamic programming (DP), this paper proposes a multi-frame target detection method based on a DP ring network (DPRN). In the proposed method, first, the target trajectory is approximated using the piecewise linear function. The velocity space partition DP (VSP-DP) is used to accumulate the merit functions of a target on each piecewise linear trajectory segment to avoid the merit function diffusion in different velocity spaces. In addition, the velocity space matching DP (VSM-DP) is employed to realize the state transition of a target between adjacent piecewise linear trajectory segments. Then, the VSP-DP and VSM-DP are used to construct a DP network (DPN). Second, to suppress the merit function diffusion further, the sequential and reverse DPNs are connected in a head-to-tail manner to form a DPRN, and the merit function of the DPRN is obtained by averaging the merit functions of the sequential and reverse DPNs. Finally, the target trajectory is obtained by tracking the extreme points of the merit functions of the DPRN. The simulation and analysis results show that the proposed DPRN combines the advantages of high detection probability of the high-order DP and high execution efficiency of the first-order DP. The proposed DPRN is suitable for radars and infrared searching and tracking systems.

DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

The detection and tracking of small targets under low signal-to-clutter ratio (SCR) has been a challenging task for infrared search and track (IRST) systems. Track-before-detect (TBD) is a widely-known algorithm which can solve this problem. However, huge computation costs and storage requirements limit its application. To address these issues, a dynamic programming (DP) and multiple hypothesis testing (MHT)-based infrared dim point target detection algorithm (DP–MHT–TBD) is proposed. It consists of three parts. (1) For each pixel in current frame, the second power optimal merit function-based DP is designed and performed in eight search areas to find the target search area that contains the real target trajectory. (2) In the target search area, the parallel MHT model is designed to save the tree-structured trajectory space, and a two-stage strategy is designed to mitigate the contradiction between the redundant trajectories and the requirements of more trajectories under low SCR. After constant false alarm segmentation of the energy accumulation map, the preliminary candidate points can be obtained. (3) The target tracking method is designed to eliminate false alarms. In this work, an efficient second power optimal merit function-based DP is designed to find the target search area for each pixel, which greatly reduces the trajectory search space. A two-stage MHT model, in which pruning for the tree-structured trajectory space is avoided and all trajectories can be processed in parallel, is designed to further reduce the hypothesis space exponentially. This model greatly reduces computational complexity and saves storage space, improving the engineering application of the TBD method. The DP–MHT–TBD not only takes advantage of the small computation amount of DP and high accuracy of an exhaustive search but also utilizes a novel structure. It can detect a single infrared point target when the SCR is 1.5 with detection probability above 90% and a false alarm rate below 0.01%.

Infrared moving dim point target detection based on spatial-temporal local contrast

Aiming at the detection of dim and small moving point targets in space-based infrared (IR) imaging systems, a detection method based on spatial-temporal local contrast (STLC) is presented. Firstly, use the spatial and temporal information to estimate the background gray value of each pixel, and obtain two prediction results respectively. Then, the spatial local contrast (SLC) and the temporal local contrast (TLC) are defined according to the original image and two estimated backgrounds. Next, the STLC is defined as the product of SLC and TLC. Finally, a threshold value is set in the STLC for target detection. Four groups of experiments are conducted, and the simulation results indicate that the proposed method has a great advantage in terms of background suppression factor (BSF) and gain of signal-to-noise (GSNR).

Infrared point target detection based on multiscale homogeneous feature fusion

In view of the difficult detection of infrared dim point target from complex backgrounds, a multiscale homogeneous feature (MHF) based multispectral image fusion detection method is proposed in this paper. Inspired by the local contrast measure (LCM), we extract two local statistical features from the perspective of the homogeneity of gray difference distribution to characterize local structure of the infrared point target. Based on these two local features, we obtain the MHF map that can effectively highlight the potential point targets and suppress the backgrounds simultaneously. For the pixel-size electronic noise (PSEN) and some similar local structures to the point target, the multispectral image fusion detection is a positive way to alleviate these interferences and promote the robustness of the dim point target detection. Experimental results on six real scenarios and synthetic scenarios demonstrate that the proposed method not only works more stably for different target sizes and brightness, but also can achieve superior detection performance compared with the state-of-art detection methods.

Novel Spatiotemporal Filter for Dim Point Targets Detection in Infrared Image Sequences

Dim point target detection is of great importance in both civil and military fields. In this paper a novel spatiotemporal filter is proposed to incorporate both the spatial and temporal features of moving dim point targets. Since targets are expected to be detected as far as possible, in this situation, they have no texture features in spatial dimensions, appearing like isolated points. Based on the attributes, potential targets are extracted by searching the local maximum point in a sliding window. And the potential targets are then correlated based on target moving patterns. After combining local maximum points and target moving patterns, structure background in infrared scene is removed. Next, the temporal profiles of infrared sense are reviewed and examined. By a new max-median filter performing on temporal profiles, the intensity of target pulse signal is extracted. Finally, each temporal profile is divided into several pieces to estimate the variance of the temporal profiles, which leads to a new detection metric. The proposed approach is tested via several infrared image sequences. The results show that our proposed method can significantly reduce the complex background in aerial infrared image sequence and have a good detection performance.

Super resolution reconstruction for infrared dim point target

A super resolution reconstruction method for dim point target detection in infrared image was presented.To achieve this,local descriptor based dense flow calculation is introduced first.Then,the high accuracy flow is used to registrate and fuse two adjacent images to generate the final high resolution image.Experiments show that,after the process,the resolution of small targets can be increased,local signal-to-noise ratio be increased and complex background be surprised.

Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems

A well-established scheme for target detection in infrared (IR) surveillance systems consists of applying a suitable decision rule on the images with background clutter previously removed. Background removal is accomplished by subtracting, from the original image, the estimate of the spatially varying background signal obtained by a background estimation algorithm (BEA). The overall target detection performance is strongly influenced by the effectiveness of the employed BEA. Particularly, the BEA and its design parameters should be chosen so as to get an accurate estimate of the background signal and to avoid biases caused by the possible presence of targets (target leakage). In this work, we present a novel method for the choice and setting of the best performing background removal technique for the detection of dim point targets. The proposed procedure is based on the simulation of dim targets implanted on an acquired sample image representing the scenario of interest. The choice of the best performing BEA is made by exploring the performance of the detection scheme for several configurations of the characteristic parameters of the BEAs. The effectiveness of the BEA selection procedure is evaluated in two case studies where real image sequences acquired by IR cameras are employed. The results confirm the benefits introduced by the proposed technique. Indeed, the performance of the IR detection system with the BEA tuned according to the proposed selection criterion is improved in that the number of false alarms is reduced up to 2 orders of magnitude compared with BEAs in other common configurations.

匹配滤波器优化设计及在红外弱小点目标检测中的应用

匹配滤波器优化设计及在红外弱小点目标检测中的应用

Moving dim point target detection with three-dimensional wide-to-exact search directional filtering

We assess the performance of a novel three-dimensional wide-to-exact search directional filtering (3DWESDF) algorithm for detecting and tracking a weak moving dim target against a cluttered background in real infrared (IR) image sequence. This paper proposed a novel 3DWESDF to decrease the 3D directional filter’s (3DDF) computational requirements and increase the target energy accumulation ability further. Prior to the filtering, a newly pre-whitening method termed three-dimensional spatial-temporal adaptive prediction filter (3DSTAPF) used here to suppress complex cluttered background. Extensive experimental results demonstrate the proposed algorithm’s ability in detecting weak dim point target against a complex cloud-cluttered background in real IR image sequence. Finally, performance comparisons of the proposed algorithm and 3DDF, on real IR image data, are presented in which the advantages of the proposed 3DWESDF filters are shown.