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

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4522
Author(s):  
Cong Zhang ◽  
Dongguang Li ◽  
Jiashuo Qi ◽  
Jingtao Liu ◽  
Yu Wang
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Infrared Image ◽  
Image Sensor ◽  
Small Target ◽  
Robust Detection ◽  
Trajectory Correction ◽  
Novel Algorithms ◽  
Small Targets ◽  
Infrared Small Target

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.

Infrared Small Target Detection Based on Morphology and SUSAN Algorithm

2019 ◽  
Vol 34 (06) ◽  
pp. 2050013 ◽  
Author(s):  
Zhiwei Hu ◽  
Yixin Su
Keyword(s):  
Target Detection ◽  
Infrared Image ◽  
Frequency Region ◽  
Guidance System ◽  
Background Suppression ◽  
Small Target ◽  
High Frequency Region ◽  
Small Target Detection ◽  
Small Targets ◽  
Infrared Small Target

Infrared small target detection is one of the key techniques in infrared imaging guidance system. The technology of infrared small target detection still needs to be further studied to improve the detection performance. This paper combines the high-pass filtering characteristics of morphological top-hat transform with SUSAN algorithm, and proposes a small infrared target detection method based on morphology and SUSAN algorithm. This method uses top-hat transform to detect the high-frequency region in infrared image, and filters out the low-frequency region in the image to implement the preliminary background suppression of infrared image. Then the SUSAN algorithm is used to detect small targets in the image after background suppression. The proposed method is applied to the single infrared image which is acquired by the infrared guidance system in the process of detecting and tracking the target under specific conditions. The experimental results show that the method is effective and can detect infrared small targets under different background.

scholarly journals Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

2019 ◽  
Vol 11 (5) ◽  
pp. 559 ◽  
Author(s):  
Tianfang Zhang ◽  
Hao Wu ◽  
Yuhan Liu ◽  
Lingbing Peng ◽  
Chunping Yang ◽  
...  
Keyword(s):  
Convex Optimization ◽  
Target Detection ◽  
Detection Efficiency ◽  
Infrared Image ◽  
Low Rank ◽  
Small Target ◽  
Small Target Detection ◽  
Lp Norm ◽  
Norm Constraint ◽  
Infrared Small Target

The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.

DETECTION OF MOVING SMALL TARGETS IN INFRARED IMAGE SEQUENCES CONTAINING CLOUD CLUTTER

2004 ◽  
Vol 18 (02) ◽  
pp. 247-260 ◽  
Author(s):  
WEI WU ◽  
JIAXIONG PENG
Keyword(s):  
Target Detection ◽  
Infrared Image ◽  
Spatial Filtering ◽  
Image Sequences ◽  
Small Target ◽  
Temporal Filtering ◽  
Small Target Detection ◽  
Small Targets ◽  
Novel Algorithm

Detecting and tracking dim moving small targets in infrared image sequences containing cloud clutter is an important area of research. The paper proposes a novel algorithm for the dim moving small target detection in cloudy background. The algorithm consists of three courses. The first course consists of the image spatial filtering and the sequence temporal filtering, it can be realized by two parallel calculative parts. The second course is the fusion and the segmentation processing. The last course is the targets acquiring and tracking, it can be achieved by the Kalman tracker. The results of our experiment prove that the algorithm is very effective.

A novel infrared small target detection method based on BEMD and local inverse entropy

2014 ◽  
Vol 66 ◽  
pp. 114-124 ◽  
Author(s):  
Zhong Chen ◽  
Song Luo ◽  
Ting Xie ◽  
Jianguo Liu ◽  
Guoyou Wang ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

A Survey on Infrared Weak Small Target Detection Method

2014 ◽  
Vol 945-949 ◽  
pp. 1558-1560
Author(s):  
Zhong Min Li ◽  
Li Fei Mei ◽  
Mao Song
Keyword(s):  
Target Detection ◽  
Infrared Imaging ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Wide Field ◽  
Small Target ◽  
Small Target Detection ◽  
Key Technologies ◽  
Small Targets ◽  
Field View

Infrared weak small target detection is one of the key technologies in the early infrared imaging guidance and wide-field view surveillance system. In the complex and low signal-to-noise ratio background, the target has only a few pixels. There is no shape and texture information to use. It brings great difficulties to the infrared weak small target detection. In this paper, we sum up the research status of infrared weak small target detection method, and analyze the key problems of infrared weak small targets detection.

scholarly journals EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

2021 ◽  
Vol 13 (16) ◽  
pp. 3200
Author(s):  
Xiaozhong Tong ◽  
Bei Sun ◽  
Junyu Wei ◽  
Zhen Zuo ◽  
Shaojing Su
Keyword(s):  
Target Detection ◽  
Information Exchange ◽  
Feature Fusion ◽  
Superior Performance ◽  
Cross Layer ◽  
Small Target ◽  
Shape Information ◽  
Small Target Detection ◽  
Small Targets ◽  
Infrared Small Target

Detecting infrared small targets lacking texture and shape information in cluttered environments is extremely challenging. With the development of deep learning, convolutional neural network (CNN)-based methods have achieved promising results in generic object detection. However, existing CNN-based methods with pooling layers may lose the targets in the deep layers and, thus, cannot be directly applied for infrared small target detection. To overcome this problem, we propose an enhanced asymmetric attention (EAA) U-Net. Specifically, we present an efficient and powerful EAA module that uses both same-layer feature information exchange and cross-layer feature fusion to improve feature representation. In the proposed approach, spatial and channel information exchanges occur between the same layers to reinforce the primitive features of small targets, and a bottom-up global attention module focuses on cross-layer feature fusion to enable the dynamic weighted modulation of high-level features under the guidance of low-level features. The results of detailed ablation studies empirically validate the effectiveness of each component in the network architecture. Compared to state-of-the-art methods, the proposed method achieved superior performance, with an intersection-over-union (IoU) of 0.771, normalised IoU (nIoU) of 0.746, and F-area of 0.681 on the publicly available SIRST dataset.

Infrared Small Target Detection Method Based on Correlation Filter

2016 ◽  
Vol 36 (5) ◽  
pp. 0512001 ◽  
Author(s):  
何玉杰 He Yujie ◽  
李敏 Li Min ◽  
张金利 Zhang Jinli ◽  
邢宇航 Xing Yuhang
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Correlation Filter ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target
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