Multiscale small-target detection techniques in single-frame infrared images:a review
Traditional radar detection is almost ineffective in complex and strong electromagnetic interference environ-ments,especially in the case of stealth targets with an extremely low-radar cross-section.In such scenarios,the infrared search and track(IRST)system,with its strong anti-interference capability and all-weather and all-airspace passive detec-tion,emerges as a viable alternative to radar in target detection.Therefore,this system is widely used,such as reconnais-sance and early warning,maritime surveillance,and precision guidance.However,the efficient and accurate use of the IRST system when identifying noncooperative small targets with minimal features,low intensity,scale variations,and unknown motion states in complex backgrounds and amidst noise interference remains a challenging task,which draws attention from scholars globally.To date,the research on infrared(IR)small-target detection technology mainly focuses on long-distance weak and small(point source)targets.However,when the scene and target scale change considerably,false alarms or missed detections easily occur.Therefore,this review focuses on the problem of IR multiscale small-target detec-tion technology.To provide a comprehensive understanding of the current research status in this domain,this review sum-marizes the field from the perspectives of algorithm principles,literature,datasets,evaluation metrics,experiments,and development directions.First,the research motivation is clarified.In practical application background,with the change in the motion state of noncooperative small targets,the scale also vary greatly,from point-like targets to small targets with fuzzy boundaries to small targets with clear outlines,which is usually difficult to distinguish properly.Therefore,to be more in line with practical application background,this review is comprehensively analyzed from the perspective of IR mul-tiscale small-target detection technology.Second,the imaging characteristics of IR multiscale small targets and back-grounds are analyzed.The targets are characterized by various types,scale changes(from point sources to small surface sources),low intensity,fuzzy boundaries,lack of texture and color information,and unknown motion status.The back-ground also exhibits characterization of by complex and variable scenes and serious noise interference.Then,the algorithm principles,related literature,and advantages and disadvantages of different algorithms for single-frame IR image using mul-tiscale small-target detection techniques are summarized.In this review,we classify IR multiscale small-target single-frame detection techniques into two main categories:classical and deep learning algorithms.The former are classified into background estimation,morphological,directional derivative/gradient/entropy,local contrast,frequency-domain,over-complete sparse representation,and sparse low-rank decomposition methods based on various modeling ideas.The latter are divided into convolutional neural networks(CNNs),classical algorithm+CNN,and CNN+Transformer based on net-work structure.In these network structures,for the adequate extraction of the IR multiscale small-target feature informa-tion,design strategies,such as contextual feature fusion,multi-scale feature fusion,dense nesting,and generative adver-sarial networks,have been introduced.To reduce the computational complexity or the limitation of data sample size,schol-ars introduced strategies,such as lightweight design and weak supervision.Classical algorithms and deep learning algo-rithms feature their on advantages and disadvantages,and thus,appropriate algorithms should be selected depending on specific problems and needs.In addition,the combination of the two types of algorithms to maximize their advantages is a current research hotspot.Finally,10 existing public datasets and 17 evaluation metrics are organized,and 7 classical algo-rithms and 15 deep learning algorithms are selected for qualitative and quantitative comparative analysis.In addition,in the research process in this field,we have integrated 20 existing classical algorithms,15 deep learning algorithms and 9 evaluation metrics in a human-computer interaction system,and the video introduction of the relevant system is published in kourenke/GUI-system-for-IR-small-target-detection(github.com).A comprehensive review of multiscale small-target detection techniques in single frame IR images resulted in 9 specific suggestions for subsequent research directions in this field.This review cannot only help beginners in rapidly comprehending the research status and development trends in this field but also serve as a reference material for other researchers.
infrared imagemulti-scale small targettarget detectionclassical algorithmdeep learning algorithm
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