A systematic literature review on object detection using near infrared and thermal images

Bustos, Nicolas; Mashhadi, Mehrsa; Lai-Yuen, Susana K.; Sarkar, Sudeep; Das, Tapas K.

doi:10.1016/j.neucom.2023.126804

Cited by 20 publications

(3 citation statements)

References 61 publications

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“…As a result, it is important to use a thermal-RGB image pair matching technique. According to the study [17], standard algorithms based on picture intensity and gradient (e.g., SIFT, SURF) frequently fail to find point correspondences in thermal-RGB image pairings. These techniques are limited to smaller linear intensity differences since they detect and characterize the feature points using the gradient information in the spatial domain, which makes them ineffective for handling the matching of thermal-RGB image pairings.…”

Section: Image Matching In Thermal Rgbmentioning

confidence: 99%

Enhanced Methodology for Building Surface Inspection Using Infrared Thermography and Numerical Simulation

Bao,

Zhang

2024

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Section: Image Matching In Thermal Rgbmentioning

confidence: 99%

Enhanced Methodology for Building Surface Inspection Using Infrared Thermography and Numerical Simulation

Bao,

Zhang

2024

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“…UAV infrared target detection describes the areas where people and cars are located in the image and the categories in the image [7]. Deep learning-based algorithms for target detection have become ubiquitous in various applications today.…”

Section: Introductionmentioning

confidence: 99%

E_YOLO:Infrared Parson-Vehicle Detection based on enhanced YOLOv8s algorithm

Wu,

Lei,

et al. 2024

Preprint

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Background: Falls are a major health concern among older adults causing around 684,000 deaths annually. By 2050, 16% of the world population is projected to be 65 years and older hence effective fall prevention strategies are crucial for the health systems. The World Falls Guidelines from 2022 classified cardiovascular disease as a potential risk factor while recommending multidomain fall-prevention intervention strategies in older community dwelling adults. Individual cardiorespiratory fitness was not further discussed leading to the aim of this scoping review which is to (1) assess the effects acute bouts of aerobic exercise on balance, fall risk or fall rate and to (2) assess the relationship between aerobic capacity, balance, fall risk and fall rate in older adults. Methods: A scoping review was conducted, including peer-reviewed articles written in in English language, dating from 1957 to February 2024 that are listed in the databases PubMed, Scopus, Web of Science, and Cochrane. Eligible for inclusion were articles using the following keywords and their synonyms: aerobic capacity, falls, balance, exercise, and older adults. The references were categorized in two topics: (1) effects of acute bouts of exercise on balance or gait performance as fall risk factors in older persons and (2) the direct relation between aerobic capacity, balance and fall rate in older adults. The findings were systematically summarized, using the PRISMA-ScR reporting guidelines. Results: Twelve articles with a total of 11,109 persons aged between 20 and 94 years old were included. Eight publications reported impaired balance after moderate to strenuous exercise in older adults. The direct relationship of aerobic capacity and fall risk was described in four publications, suggesting an inverse relationship. Conclusions: After acute bouts of aerobic exercise, an intensity-related impairment of balance in older persons was observed, a finding worth acknowledging when prescribing strenuous exercise with increased lactate levels to older adults. Since the effects lasted from 10 to 30 minutes, it is recommended to avoid balance challenging tasks after moderate to strenuous exercise in this demographic.

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“…The most common are localization systems using optical systems. Infrared systems are also often used in localization tasks [6], for example, for video surveillance in darkness.…”

Section: Introduction Motivationmentioning

confidence: 99%

Investigation of the effect of object size on accuracy of human localisation in images acquired from unmanned aerial vehicles

Tsekhmystro,

Rubel,

Lukin

2024

aktt

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The use of unmanned aerial vehicles is gaining wide popularity in various areas of research and information acquisition. More and more often, unmanned aerial vehicles are used to obtain various types of images of the Earth’s surface for its study. In particular, such data can be used in law enforcement, localization of crowds, etc. Typically, such systems operate independently of humans and provide information about objects in an automatic mode, with humans working only under the control of the aircraft. One of the main components of such systems is a neural network for localization and classification of objects, the parameters of which determine both the accuracy of the system as a whole and the design of the aircraft for shooting. In particular, the accuracy of the neural network determines the profitability of such a system, because if the accuracy is insufficient, the use of such systems will not make sense. Therefore, the main subject of research in this paper is a neural network for object localization, in particular YOLO v5, and its accuracy parameters on images obtained from unmanned aerial vehicles. The main focus of this paper is on the parameters of the neural network and the study of its metrics, which are important parameters of a trained neural network. Another important parameter for the further use of a neural network is its training parameters, as well as the data used for training. This study also pays attention to the details of the training process. The main goal of this study is to train a neural network on a selected dataset and to study the accuracy metrics of the trained neural network. The main goal of this study is to determine the dependence of localization accuracy on the area of the object, which will allow for more detailed development of unmanned systems with automatic object detection, as well as to assess the profitability of using such systems in task planning. On the basis of the data obtained, conclusions were drawn about the dependence of localization accuracy on the area of an object in images from unmanned aerial vehicles. These data can serve as a reference for unmanned aerial vehicle developers, particularly when selecting photo modules or planning the system architecture.

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A systematic literature review on object detection using near infrared and thermal images

Cited by 20 publications

References 61 publications

Enhanced Methodology for Building Surface Inspection Using Infrared Thermography and Numerical Simulation

Enhanced Methodology for Building Surface Inspection Using Infrared Thermography and Numerical Simulation

E_YOLO:Infrared Parson-Vehicle Detection based on enhanced YOLOv8s algorithm

Investigation of the effect of object size on accuracy of human localisation in images acquired from unmanned aerial vehicles

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