Invariant Features-Based Fuzzy Inference System for Animal Detection and Recognition Using Thermal Images

Meena, Divya; Agilandeeswari, L.

doi:10.1007/s40815-020-00907-9

Cited by 16 publications

(4 citation statements)

References 21 publications

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“…In clinical studies (Cociu et al, 2017), it has been indicated that using multimodal neuroimaging, such as fMRI-EEG, plays a pivotal role in diagnosing ASD. In addition, providing datasets with combined 10.3389/fnmol.2022.999605 (Chanussot et al, 1999;Davidson et al, 2001;Javed et al, 2013;Jiang et al, 2017;Meena and Agilandeeswari, 2020;Ullah et al, 2020). Hence, providing graph models based on fuzzy theory can be addressed in the future, leading to the accurate diagnosis of ASD with MRI modalities.…”

Section: Future Workmentioning

confidence: 99%

“…According to Table 2 , fuzzy-based feature extraction techniques have not been used in the diagnosis of ASD, and they can be introduced in future work. Fuzzy techniques are important in medical applications and allow researchers to develop software close to human logic ( Chanussot et al, 1999 ; Davidson et al, 2001 ; Javed et al, 2013 ; Jiang et al, 2017 ; Meena and Agilandeeswari, 2020 ; Ullah et al, 2020 ). Hence, providing graph models based on fuzzy theory can be addressed in the future, leading to the accurate diagnosis of ASD with MRI modalities.…”

Section: Future Workmentioning

confidence: 99%

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Moridian

Ghassemi

Jafari

et al. 2022

Front. Mol. Neurosci.

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Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

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Section: Future Workmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Moridian

Ghassemi

Jafari

et al. 2022

Front. Mol. Neurosci.

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“…However, manual detection of animals, habitat identification, and estimation of population size are cumbersome as they require frame-by-frame analysis of hours of video data. Some automated approaches have been proposed recently [8][9][10][11][12][13]. However, they often lack usability due to low accuracy, ineffectiveness against occlusion, visible spectrum limitations, and low detection speed.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Animals in Low-Resolution Airborne Thermal Imagery

et al. 2021

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Detecting animals to estimate abundance can be difficult, particularly when the habitat is dense or the target animals are fossorial. The recent surge in the use of thermal imagers in ecology and their use in animal detections can increase the accuracy of population estimates and improve the subsequent implementation of management programs. However, the use of thermal imagers results in many hours of captured flight videos which require manual review for confirmation of species detection and identification. Therefore, the perceived cost and efficiency trade-off often restricts the use of these systems. Additionally, for many off-the-shelf systems, the exported imagery can be quite low resolution (<9 Hz), increasing the difficulty of using automated detections algorithms to streamline the review process. This paper presents an animal species detection system that utilises the cost-effectiveness of these lower resolution thermal imagers while harnessing the power of transfer learning and an enhanced small object detection algorithm. We have proposed a distant object detection algorithm named Distant-YOLO (D-YOLO) that utilises YOLO (You Only Look Once) and improves its training and structure for the automated detection of target objects in thermal imagery. We trained our system on thermal imaging data of rabbits, their active warrens, feral pigs, and kangaroos collected by thermal imaging researchers in New South Wales and Western Australia. This work will enhance the visual analysis of animal species while performing well on low, medium and high-resolution thermal imagery.

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“…However, manual detection of pest animals, habitat identification and estimation of pest population size is cumbersome as it requires frame by frame analysis of hours of video data. Some automated approaches are proposed in recent years [2,6,19,25,31]. However, they often lack usability due to low accuracy, ineffectiveness against occlusion, limitations of the visible spectrum and low detection speed.…”

Section: Introductionmentioning

confidence: 99%

Pest Animal's Detection and Habitat Identification in Low-resolution Airborne Thermal Imagery

Ulhaq¹,

Khan²

2020

Preprint

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Invasive species are significant threats to global agriculture and food security being the major causes of crop loss. An operative biosecurity policy requires full automation of detection and habitat identification of the potential pests and pathogens. Unmanned Aerial Vehicles (UAVs) mounted thermal imaging cameras can observe and detect pest animals and their habitats, and estimate their population size around the clock. However, their effectiveness becomes limited due to manual detection of cryptic species in hours of captured flight videos, failure in habitat disclosure and the requirement of expensive high-resolution cameras. Therefore, the cost and efficiency trade-off often restricts the use of these systems. In this paper, we present an invasive animal species detection system that uses cost-effectiveness of consumer-level cameras while harnessing the power of transfer learning and an optimised small object detection algorithm. Our proposed optimised object detection algorithm named Optimised YOLO (OYOLO) enhances YOLO (You Only Look Once) by improving its training and structure for remote detection of elusive targets. Our system, trained on the massive data collected from New South Wales and Western Australia, can detect invasive species (rabbits, Kangaroos and pigs) in real-time with a higher probability of detection (85–100 %), compared to the manual detection. This work will enhance the visual analysis of pest species while performing well on low, medium and high-resolution thermal imagery, and equally accessible to all stakeholders and end-users in Australia via a public cloud.

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Invariant Features-Based Fuzzy Inference System for Animal Detection and Recognition Using Thermal Images

Cited by 16 publications

References 21 publications

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Automated Detection of Animals in Low-Resolution Airborne Thermal Imagery

Pest Animal's Detection and Habitat Identification in Low-resolution Airborne Thermal Imagery

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