2016
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The difficulty of small infrared target detection originates from the variations of infrared signatures. This paper presents the fundamental physics of infrared target variations and reports the results of variation analysis of infrared images acquired using a long wave infrared camera over a 24-hour period for different types of backgrounds.The detection parameters, such as signal-to-clutter ratio were compared according to the recording time, temperature and humidity. Through variation analysis, robust target detection methodologies are derived by controlling thresholds and designing a temporal contrast filter to achieve high detection rate and low false alarm rate. Experimental results validate the robustness of the proposed scheme by applying it to the synthetic and real infrared sequences. 1.
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李若敬, 曾瑶源, 盛卫东 et al. · ScienceDB · 2025
Infrared video satellites serve as critical tools for detecting aerial moving targets, with infrared small target detection technology forming the essential foundation for this capability. The rapid advancement of deep learning has yielded numerous single-frame detection datasets and methodologies, enabling significant progress in identifying spatially salient targets. However, moving aerial targets captured by infrared satellites typically exhibit low spatial salience and frequently occur in complex scenarios, rendering single-frame detection methods reliant on spatial information ineffective in such challenging conditions. This urgent challenge necessitates the development of multi-frame infrared small and dim target detection techniques. A major bottleneck restricting the advancement and practical application of aerial moving target detection technology has been the lack of dedicated datasets for infrared video satellite-based detection, primarily due to the difficulties and high costs associated with data collection and annotation. To address this gap and promote technological development, we construct the first infrared video satellite aerial moving target detection dataset containing many real-world scenarios, called SatVideoIRSDT dataset, and organized the inaugural detection competition based on this dataset. First, we collect 20126 frames of real infrared video satellite data from Wuhan No.1 satellite featuring aerial moving targets and annotate 29757 aerial targets. Then, we integrate two simulated infrared aerial moving target datasets with authentic space-based backgrounds to enhance scenario diversity. The resulting dataset comprises 1401 real scenarios, 122265 video frames, and 454116 annotated targets, with mask labels distinguishing different target instances to support both detection and tracking research.
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Fengdong Li, Peng Rao, Xin Chen et al. · 2024
In recent years, infrared dim-target detection has emerged as a pivotal area of research. However, most existing detection methodologies focus on single-spectral imagery. Owing to the optical diffraction limit, the image resolution obtained from different detection bands under the same conditions varies significantly. Single-spectral infrared imagery offers limited target and background features, failing to capture a comprehensive representation of the environment and thus struggling with target detection in complex backgrounds. Although multispectral image fusion can enhance the detection capability for dim infrared targets, processing across all spectral regions leads to increased computational complexity, resulting in time-consuming and redundant detection algorithms. In response to this challenge, we propose an efficient multispectral infrared dim target detection framework based on slice registration. The framework consists of a reference spectral rapid localization module (RSLM) and a multispectral feature enhancement detection network (MFE-Net). The latter includes a feature extraction module, a multispectral information-weighted fusion module (MIWF), and a detection module. Initially, potential target locations in the image are rapidly identified through the reference spectral rapid localization module, and corresponding image slices are extracted from other spectral bands based on spectral coordinate transformation. Subsequently, the multispectral feature enhancement network's feature extraction module processes these multispectral target slices to extract features from each band. Finally, the MIWF module integrates information from different spectral bands to enhance the network's sensitivity to infrared dim targets, allowing the multispectral feature enhancement detection network to conduct precise detection, reduce false alarms, and improve detection rates. The proposed method utilizes the reference spectral rapid localization module to reduce the complexity of multispectral data fusion, while the multispectral feature enhancement detection network leverages information from different bands to enrich target features, thereby enhancing the accuracy of weak infrared target detection. Experiments conducted on a comprehensive dataset demonstrate that this method outperforms other state-of-the-art methods in terms of detection probability (Pd) and false alarm rate (Fa).
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Haochen Wang, Xiwei Peng, Yuhang Bai et al. · 2025
Small target detection has great theoretical significance in the field of infrared (IR). Local contrast measure (LCM) has shown commendable performance in infrared small target detection. However, existing LCM methodologies primarily emphasize the background suppression rate, which leads to a decline in detection accuracy. In this paper, an enhanced algorithm based on the framework of Tri-layer LCM(TLLCM) is introduced, which incorporates the Difference of Gaussians (DoG) algorithm and morphological filtering operations. The proposed algorithm demonstrates an improvement in both Precision and Recall metrics, while preserving the low false alarm rate advantage inherent to TLLCM. On dataset IRSTD-1k, proposed method achieves BSF of 1934.61, SCRG of 27.74, Precision rate of 0.52 and Recall rate of 0.64.
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Shijie Liu, Chenqi Luo, Kang Yan et al. · CAAI Transactions on Intelligence Technology · 2025
ABSTRACT In the field of infrared small target detection (ISTD), the ability to detect targets in dim environments is critical, as it improves the performance of target recognition in nighttime and harsh weather conditions. The blurry contour, small size and sparse distribution of infrared small targets increase the difficulty of identifying such targets in cluttered backgrounds. Existing methodologies fall short of satisfying the requisites for the detection and categorisation of infrared small targets. To address these challenges and to enhance the precision of small object detection and classification, this paper introduces an innovative approach called location refinement and adjacent feature fusion YOLO (LA‐YOLO), which enhances feature extraction by integrating a multi‐head self‐attention mechanism (MSA). We have improved the feature fusion method to merge adjacent features, to enhance information utilisation in the path aggregation network (PAN). Lastly, we introduce supervision on the target centre points in the detection network. Empirical results on publicly available datasets demonstrate that LA‐YOLO achieves an impressive average precision (AP) of 92.46% on IST‐A and a mean average precision (mAP) of 84.82% on FLIR. The results surpass those of contemporary state‐of‐the‐art detectors, striking a balance between precision and speed. LA‐YOLO emerges as a viable and efficacious solution for ISTD, making a substantial contribution to the progression of infrared imagery analysis. The code is available at https://github.com/liusjo/LA‐YOLO .
Cited by 1Open access
Oliva M. Primera-Pedrozo, Yadira M. Soto‐Feliciano, Leonardo C. Pacheco‐Londoño et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
Fourier Transform Infrared Spectroscopy coupled to Grazing Angle Probe and operating in Reflection Absorption Infrared Spectroscopy mode has been demonstrated that can be used as a potential technique to develop new methodologies for detection of explosives on surfaces in Phase I of this research. The methodology is remote sensed, <i>in situ</i> and can detect nanograms of most target compounds. It is solvent free technique and requires no sample preparation. In this work detection of traces of neat explosives and lab made mixtures equivalent to the important explosive formulation Pentolyte has been addressed. The sample set consisted of TNT, PETN and Pentolyte mixture present in various loading concentrations. The spectral data collected was subjected to a number of statistical pre-treatment methods, including first derivative and normalization transformations to make the data more suitable for analysis. Principal Component Analysis combined with Linear Discriminant Analysis was used to classify and discriminate the target analytes.
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Mikhail Gutin, Eddy K. Tsui, Olga Gutin et al. · Defense Technical Information Center (DTIC) · 2006
Panoramic cameras offer true real-time, 360-degree coverage of the surrounding area, valuable for a variety of defense and security applications, including force protection, asset protection, asset control, security including port security, perimeter security, video surveillance, border control, airport security, coastguard operations, search and rescue, intrusion detection, and many others. Automatic detection, location, and tracking of targets outside protected area ensures maximum protection and at the same time reduces the workload on personnel, increases reliability and confidence of target detection, and enables both man-in-the-loop and fully automated system operation. Thermal imaging provides the benefits of all-weather, 24-hour day/night operation with no downtime. In addition, thermal signatures of different target types facilitate better classification, beyond the limits set by camera's spatial resolution. The useful range of catadioptric panoramic cameras is affected by their limited resolution. In many existing systems the resolution is optics-limited. Reflectors customarily used in catadioptric imagers introduce aberrations that may become significant at large camera apertures, such as required in low-light and thermal imaging. Advantages of panoramic imagers with high image resolution include increased area coverage with fewer cameras, instantaneous full horizon detection, location and tracking of multiple targets simultaneously, extended range, and others. The Automatic Panoramic Thermal Integrated Sensor (APTIS), being jointly developed by Applied Science Innovative, Inc. (ASI) and the Armament Research, Development and Engineering Center (ARDEC) combines the strengths of improved, high-resolution panoramic optics with thermal imaging in the 8 - 14 micron spectral range, leveraged by intelligent video processing for automated detection, location, and tracking of moving targets. The work in progress supports the Future Combat Systems (FCS) and the In
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Williams, Marc E. · Defense Technical Information Center (DTIC) · 2007
Almost all US aircraft losses since Vietnam have been to MANPADS(Man Portable Air Defense System). MANPADS are a principal weapon of terrorists. MANPADS are cheap, lethal, available and can obstruct US air superiority. Most military aircraft have inadequate or no Missile Warning Systems. An open-air, closed loop Test and evaluate (T&E) capability to evaluate infrared (IR) warning & countermeasures, quantify end-game effectiveness, and evaluate installed, integrated systems in military aircraft is an urgent need. T&E is conducted incrementally; installed systems are far more complex and interact with and/or depend upon many other avionic systems. The Infrared Countermeasures Assessment System (ICAS) was developed to meet his need. T&E of installed IR warning and CM equipment requires an End-to- End approach and will dramatically improve with the availability of ICAS capabilities. Evolutionary expansion of ICAS will produce a large range area in which developers and users can fly without significant constraints, conducting T&E, tactics assessment, mission rehearsal and unit training. As IR threat technology introduces multi-spectral, imaging seekers with powerful processing, ICAS will require enhancements but the fundamental facility concept can evolve to respond to any plausible future threat.
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C.C.A.M. Gielen, A. John Van Opstal, Joyce Vliegen · 2002
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G.P. de Loor · Photogrammetria · 1969
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E. Cotton · 1964
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Urmila Airsang, Sameer Sonwane, Viraj Ghorpade · 2024
Object detection is essential in applications such as healthcare (e.g. organ detection in CT and ultrasound scans), autonomous driving, and surveillance. However, smaller models like YOLOv8 nano or small often face a drop in accuracy (mAP) in challenging scenarios, such as occlusions, poor lighting, or small object sizes. While larger YOLOv8 models provide better accuracy, they are unsuitable for real-time use due to their size and computational demands. To address these limitations, we propose a novel technique that integrates temporal information through a feedback mechanism, consisting of focused mask generation and box expansion. Focused mask generation uses previous frame detections to enhance inference, while box expansion ensures better object coverage in subsequent frames. Our approach can be applied to any detection model, including those used in medical imaging or surveillance. Evaluations on benchmark datasets and real-world scenarios show a 4 percent improvement in mAP over the baseline YOLOv8 model, demonstrating enhanced detection accuracy and robustness under diverse conditions without compromising real-time performance.
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Bhekisipho Twala, Rajendra Pillay, J.H.C. Pretorius · SSRN Electronic Journal · 2022
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E.R. Davies · Elsevier eBooks · 2012
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Mohammad Javad Amiri, Shokoufe Ghazanfari, Majid Sarmad et al. · 2024
This paper presents a low-cost and AI-based automated solution for detecting hidden objects using thermal infrared imaging. We developed an affordable IR imaging setup and collected a comprehensive dataset of thermal images with different objects hidden under clothing. To overcome the challenges of low-resolution IR sensors, we implemented innovative signal processing techniques to enhance image quality, making it suitable for accurate object detection with AI. Meanwhile, a large dataset of over 1100 IR images with hidden objects was provided to train object detection algorithms, specifically YOLOv9 and RCNN. The average detection rate is close to $95 \%$, which is higher than that of the similar work. Furthermore, we investigated the physics of thermal IR radiation, analyzing the temperature behavior of various concealed objects over time.
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В. И. Кравцова · 1979
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Robert H. Pape · 1979
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Jean Dansac, Yves Cojan, Jean‐Louis Meyzonnette · 1994
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A.Justin Diraviam, S. P. Victor, G. Rajakumar et al. · International Journal of Computer and Electrical Engineering · 2011
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L'Estrange Pr · PubMed · 1969
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