Fine-Tuned Residual Network-Based Features With Latent Variable Support Vector Machine-Based Optimal Scene Classification Model for Unmanned Aerial Vehicles

Rajagopal, Aghila; Ramachandran, A.; Shankar, K.; Khari, Manju; Jha, Sudan; Lee, Yong-Ju; Joshi, Gyanendra Prasad

doi:10.1109/access.2020.3004233

Cited by 28 publications

(9 citation statements)

References 17 publications

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“…The Gray Level Co-occurrence Matrix (GLCM) is able to perform texture analysis and extract the features from images [53]. Furthermore, other methods include the Spatial and Geometric Histograms (SGH) descriptor, which is a feature descriptor for three-dimensional (3D) local surface [54,55], the visual descriptor Local Binary Pattern (LBP) [48], which is a texture operator for image classification [49] or even a novel method the Forest Fire Detection Index (FFDI) [56] which was developed first by Henry Cruz, Martina Eckert, Juan Meneses and José-Fernán Martínez [57].…”

Section: Software/methodsmentioning

confidence: 99%

Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest.

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Section: Software/methodsmentioning

confidence: 99%

Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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“…Rajagopal et al [16] developed a new optimum DL-based scene classification algorithm captured by UAV. The suggested method includes a residual network-based features extraction (RNBFE) that extract feature from the convolutional layer of a DRN system.…”

Section: Related Workmentioning

confidence: 99%

Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks

Alotaibi

Mengash²,

Negm³

et al. 2022

Applied Sciences

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Nowadays, unmanned aerial vehicles (UAVs) have gradually attracted the attention of many academicians and researchers. The UAV has been found to be useful in variety of applications, such as disaster management, intelligent transportation system, wildlife monitoring, and surveillance. In UAV aerial images, learning effectual image representation was central to scene classifier method. The previous approach to the scene classification method depends on feature coding models with lower-level handcrafted features or unsupervised feature learning. The emergence of convolutional neural network (CNN) is developing image classification techniques more effectively. Due to the limited resource in UAVs, it can be difficult to fine-tune the hyperparameter and the trade-offs amongst computation complexity and classifier results. This article focuses on the design of swarm intelligence with deep transfer learning driven aerial image classification (SIDTLD-AIC) model on UAV networks. The presented SIDTLD-AIC model involves the proper identification and classification of images into distinct kinds. For accomplishing this, the presented SIDTLD-AIC model follows a feature extraction module using RetinaNet model in which the hyperparameter optimization process is performed by the use of salp swarm algorithm (SSA). In addition, a cascaded long short term memory (CLSTM) model is executed for classifying the aerial images. At last, seeker optimization algorithm (SOA) is applied as a hyperparameter optimizer of the CLSTM model and thereby results in enhanced classification accuracy. To assure the better performance of the SIDTLD-AIC model, a wide range of simulations are implemented and the outcomes are investigated in many aspects. The comparative study reported the better performance of the SIDTLD-AIC model over recent approaches.

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“…Finally, a 1 × 1 convolution is included for improving the number of channels and improve feature extraction. If the sampling reduction process is delayed, a large activation graph is given to the convolutional layer, whereas the large activation graph maintains additional data that could give high classification accuracy [28][29][30].…”

Section: B Squeezenet Based Feature Extractionmentioning

confidence: 99%

Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare

et al. 2021

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Recently, the techniques of Internet of Things (IoT) and mobile communications have been developed to gather human and environment information data for a variety of intelligent services and applications. Remote monitoring of elderly and disabled people living in smart homes is highly challenging due to probable accidents which might occur due to daily activities such as falls. For elderly people, fall is considered as a major reason for death of post-traumatic complication. So, early identification of elderly people falls in smart homes is needed to increase the survival rate of the person or offer required support. Recently, the advent of artificial intelligence (AI), IoT, wearables, smartphones, etc makes it feasible to design fall detection systems for smart homecare. In this view, this paper presents an IoT enabled elderly fall detection model using optimal deep convolutional neural network (IMEFD-ODCNN) for smart homecare. The goal of the IMEFD-ODCNN model is to enable smartphones and intelligent deep learning (DL) algorithms to detect the occurrence of falls in the smart home. Primarily, the input video captured by the IoT devices is pre-processed in different ways like resizing, augmentation, and min-max based normalization. Besides, SqueezeNet model is employed as a feature extraction technique to derive appropriate feature vectors for fall detection. In addition, the hyperparameter tuning of the SqueezeNet model takes place using the salp swarm optimization (SSO) algorithm. Finally, sparrow search optimization algorithm (SSOA) with variational autoencoder (VAE), called SSOA-VAE based classifier is employed for the classification of fall and non-fall events. Finally, in case of fall event detected, the smartphone sends an alert to the caretakers and hospital management. The performance validation of the IMEFD-ODCNN model takes place on UR fall detection dataset and multiple cameras fall dataset. The experimental outcomes highlighted the promising performance of the IMEFD-ODCNN model over the recent methods with the maximum accuracy of 99.76% and 99.57% on the multiple cameras fall and UR fall detection dataset.

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Fine-Tuned Residual Network-Based Features With Latent Variable Support Vector Machine-Based Optimal Scene Classification Model for Unmanned Aerial Vehicles

Cited by 28 publications

References 17 publications

Computer Vision for Fire Detection on UAVs—From Software to Hardware

Computer Vision for Fire Detection on UAVs—From Software to Hardware

Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks

Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare

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