A Double-Neighborhood Gradient Method for Infrared Small Target Detection

et al. 2022

Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods.

Section: Related Workmentioning

confidence: 99%

Low-Altitude Infrared Slow-Moving Small Target Detection via Spatial-Temporal Features Measure

Rao

et al. 2022

“…The entire procedure of LSM is given in Algorithm 1. (2) for x = 1 : row do (3) for y = 1 : col do (4) Obtain the local slices R 11 and R 2m s by Equations ( 1) and ( 2); (5) Obtain the normalized slices R nor1 and R m nor s by Equations ( 3) and ( 4); (6) Calculate the matching coefficient r 1 (x,y) and determine the R 2m max by Equations ( 5)-( 7); (7) Construct the spatial-temporal joint model between I b and I b+l and calculate I v1 (x, y) by Equations ( 8)-( 14); (8) Conduct reverse matching and obtain R 31 by Equations ( 15)-( 17); (9) Calculate the normalized slice of R 31 by Equation ( 3); (10) Calculate the matching coefficient r 2 (x, y) by Equation ( 5); (11) Construct the spatial-temporal joint model between I b−l and I b and calculate I v2 (x, y) by Equations ( 8)-( 14); (12) Calculate the saliency map value I map (x, y) by Equations ( 18) and ( 19); (13) end for (14) end for (15) Obtain the saliency map I map ; (16) Calculate the adaptive threshold T by formula Equation ( 20); (17) Output the position of the aerial target.…”

Section: Adaptive Threshold Segmentationmentioning

confidence: 99%

“…The value of l was set to two after parameter analysis conducted in Section 4. To evaluate the detection effectiveness, LSMs are compared with seven state-of-the-art detection methods, including fusion saliency map (FSM) [10], double-neighborhood gradient method (DNGM) [11], neighborhood saliency map (NSM) [14], spatial-temporal local contrast filter (STLCF) [15], spatial-temporal local contrast method (STLCM) [2], spatialtemporal joint processing model (STJP) [28], and multiscale local target characteristics algorithm (MLTC) [29]. NSM, STLCF, STLCM, and STJP are existing space-based detection methods, FSM is a newly proposed detection method utilized for low-altitude slow target detection that has a similar background to space-based detection, and DNGM and MLTC are new detection methods proposed in 2020 and 2021, respectively.…”

Section: Experimental Condition and Evaluation Indexmentioning

confidence: 99%

“…The local contrast method (LCM) proposed by Chen et al [9] attracts much attention for the concise structure. Additionally, a great number of LCM-based methods [10][11][12] working on the ground-based platform have subsequently been proposed and detect small targets under complex backgrounds. Moreover, most space-based detection methods, such as local blob-like contrast map and local gradient map (LBCM-LGM) [13], neighborhood saliency map (NSM) [14], spatial-temporal local contrast method (STLCM) [2], and spatialtemporal local contrast filter (STLCF) [15], are LCM based.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

Rao

et al. 2022

The feature of a space-based infrared signal is that the intensity of clutter is much stronger than that of an aerial target. Such a feature poses a great challenge to aerial target detection since the existing infrared target detection methods are prone to enhance clutter but ignore the real target, which results in missed detection and false alarms. To tackle the challenge, we propose a concise method based on local spatial–temporal matching (LSM). Specifically, LSM mainly consists of local normalization, local direction matching, spatial–temporal joint model, and inverse matching. Local normalization aims to enhance the target to the same strength as the clutter, so that the weak target will not be ignored. After normalization, a direction-matching step is applied to estimate the moving direction of the background between the basic frame and referenced frame. Then the spatial–temporal joint model is constructed to enhance the target and suppress strong clutter. Similarly, inverse matching is conducted to further enhance the target. Finally, a salience map is obtained, on which the aerial target is extracted by the adaptive threshold segmentation. Experiments conducted on four space-based infrared datasets indicate that LSM handles the above challenge and outperforms seven state-of-the-art methods in space-based infrared aerial target detection.

“…By redefining the contrast parameters of the central region, the pixel-sized noises with high brightness (PNHB) are suppressed and the target is enhanced. Moreover, other methods optimize the rectangle structure of LCM to detect targets with different sizes, such as double-neighborhood (DLCM) [ 25 ], tri-layer (TLCM) [ 26 ], multiscale patch-based (MPCM) [ 27 ] and high-boost-based multiscale (HBMLCM) [ 28 ]. Although local contrast is further enhanced, sparse clutter elements are also highlighted [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Method with Trajectory Correction Fuze Based on Infrared Image Sensor

Zhang

et al. 2021

Due to the complexity of background and diversity of small targets, robust detection of infrared small targets for the trajectory correction fuze has become a challenge. To solve this problem, different from the traditional method, a state-of-the-art detection method based on density-distance space is proposed to apply to the trajectory correction fuze. First, parameters of the infrared image sensor on the fuze are calculated to set the boundary limitations for the target detection method. Second, the density-distance space method is proposed to detect the candidate targets. Finally, the adaptive pixel growth (APG) algorithm is used to suppress the clutter so as to detect the real targets. Three experiments, including equivalent detection, simulation and hardware-in-loop, were implemented to verify the effectiveness of this method. Results illustrated that the infrared image sensor on the fuze has a stable field of view under rotation of the projectile, and could clearly observe the infrared small target. The proposed method has superior anti-noise, different size target detection, multi-target detection and various clutter suppression capability. Compared with six novel algorithms, our algorithm shows a perfect detection performance and acceptable time consumption.