FLIR Research Articles

Evaluating Unmanned Aerial Systems for the Detection and Monitoring of Moose in Northeastern Minnesota

ABSTRACTThe use of unmanned aerial systems (UAS) for wildlife surveying and research has widely expanded in the past decade, but with varying levels of success. Applying UAS paired with Forward Looking Infrared (FLIR) technology to survey forest‐dwelling species has been particularly challenging because of unreliable animal detection. We describe our application of UAS and FLIR technology to detect GPS‐collared moose (Alces alces) and their calves in the heavily‐forested region of northeastern Minnesota, USA, during 2018 and 2019. We conducted grid‐pattern UAS thermal surveys over GPS‐collared cows during the calving seasons (April to June) of 2018 and 2019 to determine the feasibility of using a FLIR‐equipped UAS for detecting cow moose, and for quantifying the number of calves. We also collected data on environmental and flight characteristic variables to model moose detection. Our best fitting model of moose detection showed increased detection with more cloud cover at the survey site ( = 1.13, SE = 0.43), whereas increased forest canopy ( = −1.10, SE = 0.38), and vegetative greenness (enhanced vegetation index, EVI; = −1.37, SE = 0.32) both reduced detection success. By adjusting our methodology based on our detection model findings, we increased our adult moose detection success from 25% during our first season, to 85% during our second season, and calf detection from 27% to 79%, respectively. We report on our methodological improvements and identify limitations to UAS‐based wildlife research in forested systems. Overall, we found that UAS with FLIR sensing is a promising tool for quantifying moose calving success, twinning rate, and calf survival, and may be effective for monitoring the reproductive success and survival of other wildlife species in densely forested regions. © 2021 The Wildlife Society.

558 Use of Forward-Looking Infrared (FLIR) Technology to Assess Burn Depth and Healing Potential: A Systematic Review and Meta-Analysis

Abstract Introduction Burn wound depth assessments are an important component of determining patient prognosis and making appropriate management decisions. Clinical appraisal of the burn wound by an experienced burn surgeon is standard of care but has limitations. Forward-looking Infrared (FLIR) is a new technology in burn care that can provide a non-invasive, quantitative method of evaluating burn wound depth. FLIR utilizes a specialized camera that can capture the infrared emissivity of the skin, and the resulting images can be analyzed to determine burn depth and healing potential of a burn wound. Though FLIR has great potential for burn wound assessment, its use for this has not been well documented. Thus, we have conducted a systematic review and meta-analysis of the current use of FLIR technology to assess burn depth and healing potential. Methods A systematic review of the literature was performed on PubMed and Google Scholar between June 2020-August 2020 using the following keywords: thermal imaging, FLIR, forward looking infrared, burn, burn depth. Meta-analysis was performed on the mean sensitivity and specificity of the ability of FLIR to predict healing potential. Inclusion criteria were articles investigating the use of FLIR for burn wound assessments in adults, pediatric patients and animal models. Reviews and non-English articles were excluded. Results A total of 11 articles were included in the final review. Statistically significant correlations were found between FLIR and laser doppler imaging (LDI) in 3/3 clinical studies. A case report of a single patient found that FLIR was more accurate than LDI for assessing burn depth. Three articles investigated the ability of FLIR to predict healing potential, with all three reporting statistically significant results. Significant temperature differences between burnt and unburnt skin were found in 2/2 articles. FLIR was compared to clinical assessment by burn surgeons in two articles; one article found that FLIR was more accurate for assessing burn depth, while the other article found that clinical assessment was more accurate for predicting healing potential < 21 days. Mean sensitivity and specificity of the ability of FLIR to determine healing potential < 15 days was 44.5 and 98.8 respectively. Mean sensitivity and specificity of the ability of FLIR to determine healing potential < 21 days was 44.0 and 77.4 respectively. Conclusions FLIR is an accurate, simple, and cost-effective method of burn wound assessment. FLIR has been demonstrated to have significant correlations with other methods of assessing burns such as LDI and can be utilized to accurately assess burn depth and healing potential.

Assessing the Outcomes of Focused Heating of the Skin by a Long-Pulsed 1064 nm Laser with an Integrated Scanner, Infrared Thermal Guidance, and Optical Coherence Tomography.

The skin on the forearm and back of two consenting volunteers was marked and anesthetized with lidocaine with epinephrine. The parameters of a scanner-equipped LP 1064 nm Nd:YAG laser were adjusted to achieve an epidermal/superficial dermal heating of between 50°C and 60°C over a specified time course. Experimental single treatments examined various adjusted parameters including, fluence, pulse overlap, pulse duration, scan size, and pulse rate. A FLIR camera was used to record skin temperature. Outcome measures included skin temperature, post-treatment appearance, and OCT assessment of skin and vascular damage. The clinical response of each treatment was followed daily for 4 weeks. Optimal protocols initially raised the skin temperature to between 55°C and 60°C, which was carefully maintained using subsequent laser passes over a 60-second time course. Immediately post laser, clinical responses included erythema, edema, and blistering. Immediate OCT revealed increased vascularity with intact, dilated blood vessels. Prolonged exposure above 60°C resulted in sub-epidermal blistering and an absence of blood flow in the treatment area with prolonged healing. The LP 1064 nm laser can be used to achieve heat-related tissue injury, though the narrow parameters necessary for the desired endpoint require the assistance of IR thermal regulation to avoid unacceptable outcomes. The use of the laser scanner ensures precise energy delivery over a defined treatment area. Future studies might explore this as a selective hyperthermic method for the treatment of non-melanoma skin cancer. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.

Development of a Microplate Platform for High-Throughput Sample Preparation Based on Microwave Metasurfaces.

Sample preparation is one of the most time-consuming steps in diagnostic assays, particularly those involving biological samples. In this paper we report the results of finite-difference time-domain (FDTD) simulations and thermographic imaging experiments carried out with the intent of designing a microplate for rapid, high-throughput sample preparation to aid diagnostic assays. This work is based on devices known as microwave lysing triangles (MLTs) that have been proven capable of rapid sample preparation when irradiated in a standard microwave cavity. FDTD software was used to model a microplate platform as a polystyrene substrate with an array of various passive scattering elements (PSEs) of different sizes, shapes, and interelement spacings in a 2.45 GHz field identical to that of a common microwave oven. Based on the FDTD modeling, several PSE arrays were fabricated by cutting PSEs out of aluminum foil and adhering them to the bottom of regular polystyrene microplates to make prototypes. Each prototype microplate was then irradiated in a microwave cavity for a range of different times, powers, and source angles and the heating effects were observed via a forward looking infrared (FLIR) camera. Based on the results, two prototype microplate platforms have been shown to demonstrate electromagnetic and thermal enhancements similar to those seen with MLTs as well as tunable thermal responses to radio frequency (RF) irradiation.

Laser Heating and Infrared Thermography of Building Materials

In this work, a 532 nm diode CW laser is used to heat samples used as building materials at a 1 meter standoff distance while using an FLIR (Forward-Looking Infrared) thermal camera to monitor and record the heating and then cooling of each sample after lasers are switched off. The data is then analyzed using FLIR proprietary software. Since the absorption spectra of materials are unique, using multiple lasers of different wavelengths to simultaneously shine onto the sample at different locations would give enough thermal data to successfully characterize the samples within a reasonable amount of time. The results are very promising for applications involving non-destructive detection and classification of materials.

Analysis of factors determining thermal changes at osteotomy site in dental implant placement - An in-vitro study.

BackgroundHeat generation during osteotomy site preparation is a crucial factor that determines the success of dental implant placement. Among the factors that affect the heat generation, drilling speed, hand pressure and coolant temperature are independent variables. However, a relation between these three parameters and their optimal values required for the maximum outcome has not been studied so far. This study aims at finding out a relation between these factors in order to derive the optimum balance required, using an in vitro study. Material and MethodsThis in vitro experiment was performed on bovine femur. A total of 72 drillings were undertaken with the aid of a physiodispenser mounted on the test apparatus. Drill diameters of 2 mm and 2.8 mm, rotated at 1500, 2000 and 2500 rpm were included for the analysis. Hand pressures included for the comparison were 1.2 kgf and 2.4 kgf. Normal saline at room temperature, and that chilled to 00C were used for external irrigation. The temperature generated during drilling was recorded by infrared thermography using a Forward-Looking Infrared (FLIR) camera. ResultsThe highest temperature during osteotomy was observed at 2000 rpm rotational speed, 1.2 kgf operator hand pressure and saline irrigant solution at room temperature. In contrast, the lowest temperature generated was using 2500 rpm rotational speed, 2.4 kgf operator hand pressure and chilled irrigant solution. ConclusionsThe results indicate that none of the three experimented parameters generated heat above the critical temperature for bone necrosis (47°C). Thus, a high drilling speed with high hand pressure and continuous irrigation with copious amounts of cooled saline may be the ideal combination for implant osteotomy site preparation. Key words:Heat generation, dental implant drills, drilling speed, drilling pressure, irrigation, infrared thermography, thermal necrosis, osteotomy preparation.

ANALISIS KESTABILAN STATIK DAN DINAMIK PADA PESAWAT LSU-05 NG (LAPAN SURVEILLANCE UAV 05 NEW GENERATION) DENGAN MENGGUNAKAN PERANGKAT LUNAK XFLR5

Aviation in Indonesia has progressed from time to time, both in terms of existing technology and infrastructure. One of them is the development of unmanned aircraft or commonly called unmanned aircraft which is commonly abbreviated as UAV (Unmanned Aerial Vehicle). The LSU-05NG aircraft is an unmanned aircraft designed by the LAPAN Aviation Technology Center (National Institute of Aviation and Space). This aircraft is designed to meet surveillance needs or is intended for observation of a very wide area of coverage only by installing sensor devices such as cameras and FLIR (Forward Looking Infra Red). To launch this surveillance mission, the LSU05NG aircraft must have stability. Due to the light weight and also small aircraft, the stability of this aircraft will be more easily disrupted, so there is a need for stability for the LSU-05NG aircraft to carry out its mission properly. An aircraft is said to be statically stable if the lateral dimension has a value ????ɩ???? < 0, the longitudinal dimension has a value ???????????? < 0, and on the directional dimension the value ???????????? > 0. By modeling the Lsu05 new generation aircraft and analyzing the stability of the aircraft in XFLR5 software so as to obtain the roots of aerodynamic characteristics. Based on the results of analyzes conducted on XFLR5 software, the LSU-05 new generation aircraft is statically stable and changes in speed cannot affect its stability.

Infrared target detection and recognition in complex scene

The mainstream target detection network has outstanding target detection capability in high quality RGB images, but for infrared images with poor resolution, the target detection performance decreases significantly. In order to improve the performance of infrared target detection in complex scene, the following measures are adopted in this paper: Firstly, by referring to the field adaption and adopting the appropriate infrared image preprocessing means, the infrared image is closer to the RGB image, so that the mainstream target detection network can further improve the detection accuracy. Secondly, based on the one-stage target detection network YOLOv3, the algorithm replaces the original MSE loss function with the GIOU loss function. It is verified by experiments that the detection accuracy on the open infrared data set the FLIR is significantly improved. Thirdly, in view of the problem of large target size span existing in FLIR dataset, the SPP module is added with reference to the idea of the spatial pyramid to enrich the expression ability of feature map, expand the receptive field of feature map, and further improve the accuracy of target detection.

Application of forward-looking infrared (FLIR) imaging from an unmanned aerial platform in the search for decomposing remains.

Traditional methods of cadaver detection in outdoor environments include manual ground search, cadaver dogs, and manned aerial reconnaissance during daylight. These methods have limitations; however, a potential low-cost alternative may be to employ thermal imaging equipment mounted on an unmanned aerial vehicle (UAV) to detect heat emitted by insect and bacterial activity on the decomposing remains. No studies have addressed the influence of wrappings on detection of maggot mass thermal signatures nor assessed thermal detection of smaller body fragments. We addressed these knowledge gaps by utilizing a two-phase experimental approach to explore thermal detection of carcasses using UAV-mounted infrared imaging. In Phase 1, pig body fragments were deposited on the surface or shallow buried. In Phase 2, whole pig carcasses were deposited in four conditions: on the surface uncovered, wrapped in plastic or carpet, or buried. Our results demonstrated that observable heat emissions from remains corresponded to peak insect activity during active decay and could be readily detected in uncovered whole carcasses and fragments. Although plastic and carpet wrappings partly impeded detection of insect heat signatures, these materials were clearly detectable themselves because of their contrast to the background ground surface. Thermal signatures of buried partial remains and disturbed gravesoil were also observed; however, the buried whole carcass transitioned to adipocere prior to the decay stage and without any insect colonization or heat signature. These data can inform operational implementation of this technique to complement existing search strategies to offer a robust, low-cost alternative for use where scene characteristics allow.

Erratum

Erratum

Effects of environmental conditions on the use of forward‐looking infrared for bear den detection in the Alaska Arctic

AbstractIndustrial off‐road activity in winter overlaps denning habitat of polar bear (Ursus maritimus) and grizzly bear (Ursus arctos) in the North Slope oilfields of Alaska (United States). To prevent disturbance of dens, managers have used forward‐looking infrared (FLIR) cameras to detect dens, but the effectiveness of FLIR under different environmental conditions is unresolved. Our objective was to evaluate the effects of environmental variables on FLIR‐based techniques for arctic bear den detection. Using a FLIR‐equipped unmanned aircraft system (UAS), we conducted observations of artificial polar bear (APD) and grizzly bear (AGD) dens from horizontal and vertical perspectives between December 2016 and April 2017. We recorded physical characteristics of artificial dens and weather conditions present during each observation. We captured 291 images and classified each as detection or nondetection based on the number of pixels representative of a den “hot spot.” We used logistic regression to model the effects of four weather variables on the odds of detection (detection). We found that UAS‐FLIR detects APDs two times better than AGDs, and that for both species detections are four times more likely from the vertical than horizontal perspective. Lower air temperature and wind speed, and the absence of precipitation and sunlight increased detection for APDs. A 1°C increase in air temperature lowered detection by 12% for APDs and by 8% for AGDs. We recommend that UAS‐FLIR surveys be conducted early in the denning season, on cold, clear days, with calm winds, in the absence of sunlight (e.g., civil twilight). Our study further refines the application of FLIR techniques for arctic bear den detection and offers practical recommendations for optimizing detection. Putative den locations should be confirmed by a secondary method to minimize disturbance as anthropogenic activity continues in the Arctic.

Multiview Automatic Target Recognition for Infrared Imagery Using Collaborative Sparse Priors

The low resolution of infrared (IR) images makes feature extraction for classification of a challenging work. Learning-based methods, therefore, are preferred to be used on such raw imagery. In this article, in order to avoid difficulties in feature extraction, a novel multitask extension of the widely used sparse-representation-classification (SRC) method is proposed in both single and multiview set-ups. That is, the test sample could be a single IR image or images from different views. In both single-view and multiview scenarios, we try to employ collaborative spike and slab priors. This is because the traditional sparsity-inducing measures such as the $l_{0}$ -row pseudonorm makes it hard to capture the sparse structure of the coefficient matrix when expanded in terms of a training dictionary, and the priors are proved to be able to capture fairly general sparse structures. Furthermore, a joint prior and sparse coefficient estimation method (JPCEM) is proposed for the first time in this article in order to alleviate the need to handpick prior parameters required before classification. Multiple experiments are conducted on a synthetic Comanche Forward Looking IR (FLIR) Automatic Target Recognition (ATR) database collected by Army Research Lab and a challenging mid-wave IR (MWIR) image ATR database made available by the U.S. Army Night Vision and Electronic Sensors Directorate. The final results substantiate the merits of the proposed JPCEM through comparisons with other state-of-the-art methods, including both the ones based on SRC and the ones constructed using deep learning frameworks.

ENHANCEMENT OF EXTERNAL WALL DECORATION MATERIAL FOR THE BUILDING IN SAFETY INSPECTION METHOD

As buildings wear out, external wall tiles or attachments will usually fall off, sometimes causing human injuries. At present, the method employed for middle-high rise buildings is mainly the method of visual inspection. The inspection results in using this method are affected by the factors of subjectivity, safety and cost. This study aims to provide a lowercost and more efficient evaluation method for inspecting the status of buildings’ external walls. This proposed method implements Forward Looking Infrared (FLIR) technology and high-resolution photographic equipment on Unmanned Aerial Vehicle (UAV) which can improve the image recording of the detection process, as well as the overall visual detection technology, and solve the existing visual detection problem of inspectors. Also, the images detected by visual inspection and UAV high-resolution video are used to develop a suitable visual evaluation process and test table for external walls. Through the test results of several cases, the deterioration status and needs for maintenance are taken into account according to the degree of performance indicators. The findings of the study is that the proposed mechanism is more efficient and lower cost for the detection of external walls or ancillary structures’ abnormal status, which is easy to use in practice.

Efficacy of aerial forward-looking infrared surveys for detecting polar bear maternal dens.

Denned polar bears (Ursus maritimus) are invisible under the snow, therefore winter-time petroleum exploration and development activities in northern Alaska have potential to disturb maternal polar bears and their cubs. Previous research determined forward looking infrared (FLIR) imagery could detect many polar bear maternal dens under the snow, but also identified limitations of FLIR imagery. We evaluated the efficacy of FLIR-surveys conducted by oil-field operators from 2004-2016. Aerial FLIR surveys detected 15 of 33 (45%) and missed 18 (55%) of the dens known to be within surveyed areas. While greater adherence to previously recommended protocols may improve FLIR detection rates, the physical characteristics of polar bear maternal dens, increasing frequencies of weather unsuitable for FLIR detections-caused by global warming, and competing false positives are likely to prevent FLIR surveys from detecting maternal dens reliably enough to afford protections consonant with increasing global threats to polar bear welfare.

Towards an Machine Learning-Based Edge Computing Oriented Monitoring System for the Desert Border Surveillance Use Case

The design of border surveillance systems is critical for most countries in the world, having each border specific needs. This paper focuses on an Internet of Things oriented surveillance system to be deployed in the Sahara Desert, which is composed of many unattended fixed platforms, where the nodes in the edge have a Forward Looking InfraRed (FLIR) camera for field monitoring. To reduce communications and decentralise the processing, IR images should be fully computed on the edge by an Automated Target Recognition (ATR) algorithm, tracking and identifying targets of interest. As edge nodes are constrained in energy and computing capacity, this work proposes two ATR systems to be executed in low-power microprocessors. Both proposals are based on using Bag-of-Features for feature extraction and a supervised algorithm for classification, both differing in segmenting the InfraRed image in regions of interest or working directly with the whole image. Both proposals are successfully applied to infer about a dataset generated to this end, getting a trade-off between computing cost and detection capacity. As a result, the authors obtained a detection capacity of up to 97% and a frame rate of up to 5.71 and 59.17, running locally on the edge device and the workstation, respectively.

CNN-Based Person Detection Using Infrared Images for Night-Time Intrusion Warning Systems.

Night-time surveillance is important for safety and security purposes. For this reason, several studies have attempted to automatically detect people intruding into restricted areas by using infrared cameras. However, detecting people from infrared CCTV (closed-circuit television) is challenging because they are usually installed in overhead locations and people only occupy small regions in the resulting image. Therefore, this study proposes an accurate and efficient method for detecting people in infrared CCTV images during the night-time. For this purpose, three different infrared image datasets were constructed; two obtained from an infrared CCTV installed on a public beach and another obtained from a forward looking infrared (FLIR) camera installed on a pedestrian bridge. Moreover, a convolution neural network (CNN)-based pixel-wise classifier for fine-grained person detection was implemented. The detection performance of the proposed method was compared against five conventional detection methods. The results demonstrate that the proposed CNN-based human detection approach outperforms conventional detection approaches in all datasets. Especially, the proposed method maintained F1 scores of above 80% in object-level detection for all datasets. By improving the performance of human detection from infrared images, we expect that this research will contribute to the safety and security of public areas during night-time.

DeepTarget: An Automatic Target Recognition Using Deep Convolutional Neural Networks

Automatic target recognition (ATR) is an important part for many computer vision applications. Despite the extensive research which has been carried out in this area for many years, there is no ATR system which performs well on all applications. Recently, different object recognition frameworks have been proposed which yield a high performance in baseline databases. However, our experiments showed that they can fail in real-world scenarios, when dealing with a limited number of data samples. In this paper, we propose a new ATR system, based on deep convolutional neural network (DCNN), to detect the targets in forward looking infrared (FLIR) scenes and recognize their classes. In our proposed ATR framework, a fully convolutional network is trained to map the input FLIR imagery data to a fixed stride correspondingly-sized target score map. The potential targets are identified by applying a threshold on the target score map. Finally, the corresponding regions centered at these target points are fed to a DCNN to classify them into different target types while at the same time rejecting the false alarms. The proposed architecture achieves a significantly better performance in comparison with that of the state-of-the-art methods on two large FLIR image databases.

Detection, Localization and Analysis of Oil Spills in Water Through Wireless Thermal Imaging and Spectrometer Based Intelligent System

In this study, a marine robotic system is designed to locate and to quantify the surface water pollution in lakes and ponds with the consideration of the influences of neighboring buildings and trees. The proposed system consists of an aerial robot and a marine robot, which are actuated by pattern recognition at the base station with safe path planning. In the designed system,on quadrotor a Forward Looking Infra Red (FLIR) thermal imaging camera is employed to spot oil spills following a neural network method, while a wireless digital camera is used to identify the apropos path by imparting edge detection techniques. The surrounding buildings and trees dysfunction the digital camera’s reflectivity, thereby disparate from optimal path planning. To dwindle the experimental impediment, the Voronoi algorithm is employed by fractionating the experiment’s domain into small sub-sections. After that, Dijkstra’s algorithm is considered to assure the optimal path planning from the starting point to the target. Fuzzy logic is introduced to direct boats towards the target. Global positioning system (GPS), sonar and navigation sensors based boat follow the trajectory defined by the aerial robot towards target point and take samples for quantification test through the spectrometer. These results then sent to the base station for pattern recognition using neural networks. Such systems can efficiently and effectively navigate the water pollution problems as disaster response.

Geophysical Assessment of a Proposed Landfill Site in Fredericktown, Missouri.

In cooperation with the U.S. Environmental Protection Agency (EPA), the U.S. Geological Survey (USGS) collected numerous types of geophysical data at a site in Fredericktown, Missouri, in June 2018. Various geophysical surveys were collectively used to help evaluate the overall suitability of the site for use as a mine waste-soil and sediment repository, and to evaluate the suite of geophysical methods for rapid collection and preliminary assessment of sites with shallow sediments. Land-based geophysical methods, which included frequency-domain electromagnetic induction (FDEM), electrical resistivity tomography (ERT), horizontal-to-vertical spectral ratio passive seismic (HVSR), and shear-wave refraction, were used to determine the depths to crystalline bedrock and characterize the overlying unconsolidated sediments (or regolith). Water-borne FDEM profiles and forward-looking infrared (FLIR) thermal image surveys were conducted along the Fredericktown City Lake shoreline to identify locations of potential interactions between groundwater and surface water. Sediment temperature profilers were installed at two locations along the shoreline to characterize shallow unconsolidated sediment thermal properties and support the interpretation of the other geophysical surveys. Geophysical reconnaissance methods including the FDEM and HVSR methods, were used to rapidly evaluate the vertical and lateral extent of overburden, or unconsolidated sediments, overlying the bedrock at the site. The results of these methods were compared to reference geophysical methods of ERT and shear-wave refraction surveys that have greater accuracy and are more labor intensive and time-consuming. A goal of the project was the evaluation of the validity and reliability of this suite of reconnaissance geophysical methods as a means by which shallow (less than 3 meters (m)) sediments can be rapidly assessed. Two orthogonal ERT survey profiles, which used 28 electrodes spaced 1 m apart in dipole-dipole and combined Wenner-Schlumberger configurations, were collected to determine the subsurface resistivity. The results were inverted to produce electrical resistivity profiles that were compared to the FDEM and HVSR survey results. The FDEM data were collected along cleared paths through the proposed disposal cell locations. The data were inverted to generate depth-dependent estimates of electrical conductivity along the transects. An analysis of the depth of investigation (DOI) indicated the FDEM imaged to depths of about 3 m below land surface. The ERT, FDEM, and HVSR indicated the depth to crystalline bedrock was approximately 1.5 m below land surface with shallower and deeper areas. Results from this investigation indicate this suite of methods will likely perform well at sites with shallow depths to bedrock and strong conductivity and acoustic impedance contrasts, where the FDEM and HVSR methods can provide estimates of the depth to bedrock, and ERT and shear-wave refraction surveys might not be worth the added time and expense.

A robust FLIR target detection employing an auto-convergent pulse coupled neural network

ABSTRACTAutomatic target detection (ATD) of a small target along with its true shape from highly cluttered forward-looking infrared (FLIR) imagery is crucial. FLIR imagery is low contrast in nature, which makes it difficult to discriminate the target from its immediate background. Here, pulse-coupled neural network (PCNN) is extended with auto-convergent criteria to provide an efficient ATD tool. The proposed auto-convergent PCNN (AC-PCNN) segments the target from its background in an adaptive manner to identify the target region when the target is camouflaged or contains higher visual clutter. Then, selection of region of interest followed by template matching is augmented to capture the accurate shape of a target in a real scenario. The outcomes of the proposed method are validated through well-known statistical methods and found superior performance over other conventional methods.