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Adversarial Defense Technology for Small Infrared Targets
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作者 Tongan Yu Yali Xue +2 位作者 Yiming He Shan Cui Jun Hong 《Computers, Materials & Continua》 SCIE EI 2024年第10期1235-1250,共16页
With the rapid development of deep learning-based detection algorithms,deep learning is widely used in the field of infrared small target detection.However,well-designed adversarial samples can fool human visual perce... With the rapid development of deep learning-based detection algorithms,deep learning is widely used in the field of infrared small target detection.However,well-designed adversarial samples can fool human visual perception,directly causing a serious decline in the detection quality of the recognition model.In this paper,an adversarial defense technology for small infrared targets is proposed to improve model robustness.The adversarial samples with strong migration can not only improve the generalization of defense technology,but also save the training cost.Therefore,this study adopts the concept of maximizing multidimensional feature distortion,applying noise to clean samples to serve as subsequent training samples.On this basis,this study proposes an inverse perturbation elimination method based on Generative Adversarial Networks(GAN)to realize the adversarial defense,and design the generator and discriminator for infrared small targets,aiming to make both of them compete with each other to continuously improve the performance of the model,find out the commonalities and differences between the adversarial samples and the original samples.Through experimental verification,our defense algorithm is not only able to cope with multiple attacks but also performs well on different recognition models compared to commonly used defense algorithms,making it a plug-and-play efficient adversarial defense technique. 展开更多
关键词 Adversarial defense adversarial robustness small infrared targets transferable perturbation GAN
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A Novel Filtering-Based Detection Method for Small Targets in Infrared Images
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作者 Sanxia Shi Yinglei Song 《Computers, Materials & Continua》 SCIE EI 2024年第11期2911-2934,共24页
Infrared small target detection technology plays a pivotal role in critical military applications,including early warning systems and precision guidance for missiles and other defense mechanisms.Nevertheless,existing ... Infrared small target detection technology plays a pivotal role in critical military applications,including early warning systems and precision guidance for missiles and other defense mechanisms.Nevertheless,existing traditional methods face several significant challenges,including low background suppression ability,low detection rates,and high false alarm rates when identifying infrared small targets in complex environments.This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach.The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.In the first step,the infrared image is filtered by a new filter kernel and the results of filtering are normalized.In the second step,an adaptive thresholding method is utilized to determine the pixels in small targets.In the final step,a fuzzy C-mean clustering algorithm is employed to group pixels in the same target,thus yielding the detection results.The results obtained from various real infrared image datasets demonstrate the superiority of the proposed method over traditional approaches.Compared with the traditional method of state of the arts detection method,the detection accuracy of the four sequences is increased by 2.06%,0.95%,1.03%,and 1.01%,respectively,and the false alarm rate is reduced,thus providing a more effective and robust solution. 展开更多
关键词 Gaussian filtering infrared small target detection fuzzy C-means clustering ROBUSTNESS
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Improved Weighted Local Contrast Method for Infrared Small Target Detection
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作者 Pengge Ma Jiangnan Wang +3 位作者 Dongdong Pang Tao Shan Junling Sun Qiuchun Jin 《Journal of Beijing Institute of Technology》 EI CAS 2024年第1期19-27,共9页
In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted... In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV). 展开更多
关键词 infrared small target unmanned aerial vehicles(UAV) local contrast target detection
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Infrared Small Target Detection Algorithm Based on ISTD-CenterNet
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作者 Ning Li Shucai Huang Daozhi Wei 《Computers, Materials & Continua》 SCIE EI 2023年第12期3511-3531,共21页
This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet(ISTD-CenterNet)network for detecting small infrared targets in complex environments.The method eliminates the n... This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet(ISTD-CenterNet)network for detecting small infrared targets in complex environments.The method eliminates the need for an anchor frame,addressing the issues of low accuracy and slow speed.HRNet is used as the framework for feature extraction,and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects.A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire infrared image.Besides,an improved sensory field enhancement module is designed to leverage semantic information in low-resolution feature maps,and a convolutional attention mechanism module is used to increase network stability and convergence speed.Comparison experiments conducted on the infrared small target data set ESIRST.The experiments show that compared to the benchmark network CenterNet-HRNet,the proposed ISTD-CenterNet improves the recall by 22.85%and the detection accuracy by 13.36%.Compared to the state-of-the-art YOLOv5small,the ISTD-CenterNet recall is improved by 5.88%,the detection precision is improved by 2.33%,and the detection frame rate is 48.94 frames/sec,which realizes the accurate real-time detection of small infrared targets. 展开更多
关键词 infrared small target detection CenterNet data enhancement feature enhancement attention mechanism
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PF-YOLOv4-Tiny: Towards Infrared Target Detection on Embedded Platform
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作者 Wenbo Li Qi Wang Shang Gao 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期921-938,共18页
Infrared target detection models are more required than ever before to be deployed on embedded platforms,which requires models with less memory consumption and better real-time performance while considering accuracy.T... Infrared target detection models are more required than ever before to be deployed on embedded platforms,which requires models with less memory consumption and better real-time performance while considering accuracy.To address the above challenges,we propose a modified You Only Look Once(YOLO)algorithm PF-YOLOv4-Tiny.The algorithm incorpo-rates spatial pyramidal pooling(SPP)and squeeze-and-excitation(SE)visual attention modules to enhance the target localization capability.The PANet-based-feature pyramid networks(P-FPN)are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy.To lighten the network,the standard convolutions other than the backbone network are replaced with depthwise separable convolutions.In post-processing the images,the soft-non-maximum suppression(soft-NMS)algorithm is employed to subside the missed and false detection problems caused by the occlusion between targets.The accuracy of our model can finally reach 61.75%,while the total Params is only 9.3 M and GFLOPs is 11.At the same time,the inference speed reaches 87 FPS on NVIDIA GeForce GTX 1650 Ti,which can meet the requirements of the infrared target detection algorithm for the embedded deployments. 展开更多
关键词 infrared target detection visual attention module spatial pyramid pooling dual-path feature fusion depthwise separable convolution soft-NMS
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Novel detection method for infrared small targets using weighted information entropy 被引量:13
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作者 Xiujie Qu He Chen Guihua Peng 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第6期838-842,共5页
This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy (LMWIE), is proposed to suppress background. Then, the g... This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy (LMWIE), is proposed to suppress background. Then, the grey value of targets is enhanced by calculating the local energy. Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets. Experimental results show that compared with the adaptive Butterworth high-pass filter method, the proposed algorithm is more effective and faster for the infrared small target detection. 展开更多
关键词 infrared small target detection local mutation weight-ed information entropy (LMWIE) grey value of target adaptivethreshold.
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Using deep learning to detect small targets in infrared oversampling images 被引量:15
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作者 LIN Liangkui WANG Shaoyou TANG Zhongxing 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第5期947-952,共6页
According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network(CNN) is designed to automatically extrac... According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network(CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a cluttersuppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3–4 orders of magnitude, and has more powerful target detection performance. 展开更多
关键词 infrared small target detection OVERSAMPLING deep learning convolutional neural network(CNN)
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CAFUNeT:A small infrared target detection method in complex backgrounds
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作者 孙海蓉 康莉 HUANG Jianjun 《中国体视学与图像分析》 2023年第4期332-348,共17页
Small infrared target detection has widespread applications in various fields including military,aviation,and medicine.However,detecting small infrared targets in complex backgrounds remains challenging.To detect smal... Small infrared target detection has widespread applications in various fields including military,aviation,and medicine.However,detecting small infrared targets in complex backgrounds remains challenging.To detect small infrared targets,we propose a variable-structure U-shaped network referred as CAFUNet.A central differential convolution-based encoder,ASPP,an Attention Fusion module,and a decoder module are the critical components of the CAFUNet.The encoder module based on central difference convolution effectively extracts shallow detail information from infrared images,complemented by rich contextual information obtained from the deep features in the decoder module.However,the direct fusion of the shallow detail features with semantic features may lead to feature mismatch.To address this,we incorporate an Attention Fusion(AF)module to enhance the network performance further.We performed ablation studies on each module to evaluate its effectiveness.The results show that our proposed algorithm outperforms the state-of-the-art methods on publicly available datasets. 展开更多
关键词 small infrared target detection central difference convolution ASPP AF
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Infrared modeling and imaging simulation of midcourse ballistic targets based on strap-down platform 被引量:2
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作者 Changzhen Qiu Zhiyong Zhang +1 位作者 Huanzhang Lu Kaifeng Zhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第5期776-785,共10页
An infrared (IR) imaging simulation framework based on the strap-down platform is proposed for midcourse ballistic targets. It overcomes the shortcoming of the existing algorithms, which cannot simulate IR imaging f... An infrared (IR) imaging simulation framework based on the strap-down platform is proposed for midcourse ballistic targets. It overcomes the shortcoming of the existing algorithms, which cannot simulate IR imaging from the entire midcourse process. The proposed framework includes three steps, target characteristic modeling, motion modeling, and imaging modeling. In imaging modeling, the staring focal plane is taken into account due to its wide employment. In order to obtain IR images of high fidelity, especially that the fluctuation of the target signal-to-noise ratio (SNR) is reasonably similar to the actual one, this paper proposes an improved IR imaging simulation method. The proposed method considers two critical factors of the pixel plane, occupy-empty ratio and defect elements, which affect the imaging of targets markedly but are neglected in previous work. Finally, the IR image sequence of high fidelity is obtained. And the correlative parameters of simulation can be set according to the given scene. Thus the generated images can satisfy the needs of algorithms validation for tracking and recognition. 展开更多
关键词 strap-down platform midceurse ballistic target infrared (IR) staring focal plane.
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Micro-motion dynamics analysis of ballistic targets based on infrared detection 被引量:1
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作者 Junliang Liu Yanfang Li +2 位作者 Shangfeng Chen Huanzhang Lu Bendong Zhao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2017年第3期472-480,共9页
The dynamic characteristics related to micro-motions, such as mechanical vibration or rotation, play an essential role in classifying and recognizing ballistic targets in the midcourse, and recent researches explore w... The dynamic characteristics related to micro-motions, such as mechanical vibration or rotation, play an essential role in classifying and recognizing ballistic targets in the midcourse, and recent researches explore ways of extracting the micro-motion features from radar signals of ballistic targets. In this paper, we focus on how to investigate the micro-motion dynamic characteristics of the ballistic targets from the signals based on infrared (IR) detection, which is mainly achieved by analyzing the periodic fluctuation characteristics of the target IR irradiance intensity signatures. Simulation experiments demonstrate that the periodic characteristics of IR signatures can be used to distinguish different micro motion types and estimate related parameters. Consequently, this is possible to determine the micro-motion dynamics of ballistic targets based on IR detection. 展开更多
关键词 micro-motion dynamics infrared (IR) signatures target recognition parameters estimation
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Calculation of the heat flux in the lower divertor target plate using an infrared camera diagnostic system on the experimental advanced superconducting tokamak 被引量:4
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作者 Zhi-Xue Cui Xin Li +3 位作者 Shuang-Bao Shu Jia-Rong Luo Mei-Wen Chen Yu-Zhong Zhang 《Nuclear Science and Techniques》 SCIE CAS CSCD 2019年第6期63-69,共7页
During the discharging of Tokamak devices, interactions between the core plasma and plasma-facing components (PFCs) may cause exorbitant heat deposition in the latter. This poses a grave threat to the lifetimes of PFC... During the discharging of Tokamak devices, interactions between the core plasma and plasma-facing components (PFCs) may cause exorbitant heat deposition in the latter. This poses a grave threat to the lifetimes of PFCs materials. An infrared (IR) diagnostic system consisting of an IR camera and an endoscope was installed on an Experimental Advanced Superconducting Tokamak (EAST) to monitor the surface temperature of the lower divertor target plate (LDTP) and to calculate the corresponding heat flux based on its surface temperature and physical structure, via the finite element method. First, the temperature obtained by the IR camera was calibrated against the temperature measured by the built-in thermocouple of EAST under baking conditions to determine the true temperature of the LDTP. Next, based on the finite element method, a target plate model was built and a discretization of the modeling domain was carried out. Then, a heat conduction equation and boundary conditions were determined. Finally, the heat flux was calculated. The new numerical tool provided results similar to those for DFLUX;this is important for future work on related physical processes and heat flux control. 展开更多
关键词 EAST DIVERTOR target PLATE infrared camera Heat FLUX Finite element analysis
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An infrared target intrusion detection method based on feature fusion and enhancement 被引量:10
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作者 Xiaodong Hu Xinqing Wang +3 位作者 Xin Yang Dong Wang Peng Zhang Yi Xiao 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2020年第3期737-746,共10页
Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infr... Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed.This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels.Among them,LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library,while motion frame difference method can detect the moving regions of the image,improve the integrity of target regions such as camouflage,sheltering and deformation.In order to integrate the advantages of the two methods,the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced.The enhancement module of the network strengthened and screened the targets,and realized the background suppression of infrared images.Based on the experiments,the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated,and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets,and it’s detection performance was far better than the comparison algorithm.The experiment results indicated that,compared with traditional infrared target detection methods,the proposed method could detect the infrared invasion target more accurately,and suppress the background noise more effectively. 展开更多
关键词 target intrusion detection Convolutional neural network Feature fusion infrared target
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Theoretical analysis of the surface temperature regulation of an infrared false target subjected to periodical ambient conditions 被引量:2
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作者 Shi-min LI Hong YE Qi-zhao LIN 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2016年第5期360-366,共7页
Infrared false target is an important mean to induce the infrared-guided weapons,and the key issue is how to keep the surface temperature of the infrared false target to be the same as that of the object to be protect... Infrared false target is an important mean to induce the infrared-guided weapons,and the key issue is how to keep the surface temperature of the infrared false target to be the same as that of the object to be protected.One-dimensional heat transfer models of a metal plate and imitative material were established to explore the influences of the thermophysical properties of imitative material on the surface temperature difference(STD) between the metal plate and imitative material which were subjected to periodical ambient conditions.It is elucidated that the STD is determined by the imitative material’s dimensionless thickness(dim*,) and the thermal inertia(Pim).When dim* is above 1.0,the STD is invariable as long as Pim is a constant.And if the dimensionless thickness of metal plate(d,m*) is also larger than 1.0,the STD approaches to zero as long as Pimis the same as the thermal inertia of metal plate(Pm).When dim* is between 0.08 and 1,the STD varies irregularly with Pim and dim*.However,if dm* is also in the range of 0.08-1,the STD approaches to zero on condition that Pim=Pm and dim*= dm*.If dim*,is below 0.08,the STD is unchanged when Pimdim* is a constant.And if dm* is also less than 0.08,the STD approaches to zero as long as Pimdim* = Pmdm*.Furthermore,an applicationoriented discussion indicates that the imitative material can be both light and thin via the application of the phase change material with a preset STD because of its high specific heat capacity during the phase transition process. 展开更多
关键词 infrared false target Surface temperature Periodical ambient conditions Thermal inertia Dimensionless thickness
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A pixel-level local contrast measure for infrared small target detection 被引量:3
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作者 Zhao-bing Qiu Yong Ma +3 位作者 Fan Fan Jun Huang Ming-hui Wu Xiao-guang Mei 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2022年第9期1589-1601,共13页
Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregul... Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure(PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously.With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker(RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed. 展开更多
关键词 infrared(IR)small target Irregular size Random walker(RW) Pixel-level local contrast measure(PLLCM)
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Deep Learning Based Target Tracking and Classification for Infrared Videos Using Compressive Measurements 被引量:2
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作者 Chiman Kwan Bryan Chou +1 位作者 Jonathan Yang Trac Tran 《Journal of Signal and Information Processing》 2019年第4期167-199,共33页
Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random ... Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one particular type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled. Even in such a special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using short wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) videos demonstrated the efficacy of the proposed approach even though the training data are very scarce. 展开更多
关键词 target Tracking Classification COMPRESSIVE Sensing SWIR MWIR LWIR YOLO ResNet infrared VIDEOS
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Infrared image simulation of ground maneuver target and scene
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作者 穆成坡 彭明松 +2 位作者 高翔 张睿恒 董清先 《Journal of Beijing Institute of Technology》 EI CAS 2016年第2期247-253,共7页
Infrared scene simulation has extensive applications in military and civil fields. Based on a certain experimental environment,object-oriented graphics rendering engine( OGRE) is utilized to simulate a real three-di... Infrared scene simulation has extensive applications in military and civil fields. Based on a certain experimental environment,object-oriented graphics rendering engine( OGRE) is utilized to simulate a real three-dimensional infrared complex scene. First,the target radiation of each part is calculated based on our experimental data. Then through the analysis of the radiation characteristics of targets and related material,an infrared texture library is established and the 3ds Max software is applied to establish an infrared radiation model.Finally,a real complex infrared scene is created by using the OGRE engine image rendering technology and graphic processing unit( GPU) programmable pipeline technology. The results show that the simulation images are very similar to real images and are good supplements to real data. 展开更多
关键词 maneuver target target scene image simulation infrared image object-orientedgraphics rendering engine (OGRE)
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DESIGN AND IMPLEMENTATION OF AN INFRARED SCENE TARGET SIMULATOR
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作者 Tang Shuo Sun Li(College of Astronautics, Northwestern Polytechnical University, Xi’an, China, 710072)Wu Yonggang Wu Genshui(Eletric-Optical Research Center of MAS, Luoyang, Henan, China, 471009) 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 1997年第1期46-50,共5页
A general review is presented of the development of infrared scene targetsimulators; special characters and advantages of scene simulators based on computer-MOSresistor array are discussed by comparing with scaled mod... A general review is presented of the development of infrared scene targetsimulators; special characters and advantages of scene simulators based on computer-MOSresistor array are discussed by comparing with scaled model simulators; the system principle, structure, key technologies, software and hardware implementation are described. 展开更多
关键词 infrared imagery target simulators RESISTORS
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Dim Moving Small Target Detection by Local and Global Variance Filtering on Temporal Profiles in Infrared Sequences
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作者 Chen Hao Liu Delian 《航空兵器》 CSCD 北大核心 2019年第6期43-49,共7页
In this paper, the temporal different characteristics between the target and background pixels are used to detect dim moving targets in the slow-evolving complex background. A local and global variance filter on tempo... In this paper, the temporal different characteristics between the target and background pixels are used to detect dim moving targets in the slow-evolving complex background. A local and global variance filter on temporal profiles is presented that addresses the temporal characteristics of the target and background pixels to eliminate the large variation of background temporal profiles. Firstly, the temporal behaviors of different types of image pixels of practical infrared scenes are analyzed.Then, the new local and global variance filter is proposed. The baseline of the fluctuation level of background temporal profiles is obtained by using the local and global variance filter. The height of the target pulse signal is extracted by subtracting the baseline from the original temporal profiles. Finally, a new target detection criterion is designed. The proposed method is applied to detect dim and small targets in practical infrared sequence images. The experimental results show that the proposed algorithm has good detection performance for dim moving small targets in the complex background. 展开更多
关键词 small target detection infrared image sequences complex background temporal profile variance filtering
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Infrared Image Target Segmentation Processing Based On Space-Time Combination 被引量:3
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作者 Chuanwen Liu 《通讯和计算机(中英文版)》 2006年第3期102-108,共7页
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Infrared Image Small Target Detection Based on Bi-orthogonal Wavelet and Morphology
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作者 迟健男 张朝晖 +1 位作者 王东署 郝彦爽 《Defence Technology(防务技术)》 SCIE EI CAS 2007年第3期203-208,共6页
An image multi-scale edge detection method based on anti-symmetrical bi-orthogonal wavelet is given in theory. Convolution operation property and function as a differential operator are analyzed,which anti-symmetrical... An image multi-scale edge detection method based on anti-symmetrical bi-orthogonal wavelet is given in theory. Convolution operation property and function as a differential operator are analyzed,which anti-symmetrical bi-orthogonal wavelet transform have. An algorithm for wavelet reconstruction in which multi-scale edge can be detected is put forward. Based on it, a detection method for small target in infrared image with sea or sky background based on the anti-symmetrical bi-orthogonal wavelet and morphology is proposed. The small target detection is considered as a process in which structural background is removed, correlative background is suppressed, and noise is restrained. In this approach, the multi-scale edge is extracted by means of the anti-symmetrical bi-orthogonal wavelet decomposition. Then, module maximum chains formed by complicated background of clouds, sea wave and sea-sky-line are removed, and the image background becomes smoother. Finally, the morphology based edge detection method is used to get small target and restrain undulate background and noise. Experiment results show that the approach can suppress clutter background and detect the small target effectively. 展开更多
关键词 控制导航系统 航天器 边缘方向 红外线图像 小目标探测
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