Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model

Pustokhina, Irina V.; Pustokhin, Denis A.; Pareek, Piyush Kumar; Gupta, Deepak; Khanna, Ashish; Shankar, K.

doi:10.1002/dac.4786

Cited by 28 publications

(14 citation statements)

References 21 publications

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“…Clustering is a well-known energy-efficient method in which nodes select a cluster head. In [15], a deep learning model is proposed to manage cluster-based UAV networks in an energy-effective way. The model adopts a cluster-level fuzzy logic technique based on a residual network.…”

Section: Energy-efficient Machine Learning Approaches For Uavsmentioning

confidence: 99%

Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

Raza

Osman

Ferrini

et al. 2021

Drones

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In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and completion of energy-demanding tasks. The possibility of providing UAVs with advanced decision-making capabilities in an energy-effective way would be extremely beneficial. In this paper, we propose a practical solution to this problem that exploits deep learning on the edge. The developed system integrates an OpenMV microcontroller into a DJI Tello Micro Aerial Vehicle (MAV). The microcontroller hosts a set of machine learning-enabled inference tools that cooperate to control the navigation of the drone and complete a given mission objective. The goal of this approach is to leverage the new opportunistic features of TinyML through OpenMV including offline inference, low latency, energy efficiency, and data security. The approach is successfully validated on a practical application consisting of the onboard detection of people wearing protection masks in a crowded environment.

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Section: Energy-efficient Machine Learning Approaches For Uavsmentioning

confidence: 99%

Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

Raza

Osman

Ferrini

et al. 2021

Drones

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show abstract

“…Along with the flipping, rotating, shifting, random cropping, and resizing, we have also implemented random erasing [30]. Random erasing is another efficient data augmentation technique that helps to directly reduce over-fitting by altering the input space and thus forcing the model to find other descriptive characteristics.…”

Section: ) Random Erasingmentioning

confidence: 99%

“…In order to build a strong single model for vehicle make and model recognition on the Stanford Cars dataset, we adopted the ensemble learning approach. The experiments were performed with additional data augmentation techniques other than just standard data augmentation schemes such as random erasing [30] and mixup [32]. Later, we also implemented ensemble learning [29] using bagging with a bag size of five.…”

Section: A Ensembling the Homogeneous Modelsmentioning

confidence: 99%

An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

et al. 2021

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Vehicle make and model recognition is an important component of the Intelligent Transport System (ITS), which plays an essential role in vehicle surveillance and traffic monitoring. In this paper, we evaluated the performance of the recent deep neural networks for vehicle recognition to identify 196 different types of vehicles based on their make, model, and year using Stanford Cars data. Transfer learning has been employed to reduce the training time and also added a dropout of 0.5 at the last dense layer before the output layer for improved generalization performance on an applied dataset. Different augmentation methods such as random erasing, horizontal flip, random crop, resize, rotate, shear, and sharpen were applied to the training dataset to increase the number of images. Besides, optimization methods namely mixup and cyclical learning rate were also investigated to train these deep neural networks to avoid overfitting. Finally, the proposed ensemble learning framework using k-fold cross-validation has resulted in the improved accuracy of image classification for the vehicle make and model recognition. The final achieved accuracy is 93.96% through ensembling homogeneous models of DenseNet201, which is higher than previous stateof-the-art approaches for the vehicle make and model recognition. Simultaneously, it has also improved the performance of other models where MobileNetV2 has shown the highest improvement with an increase of 2.8% in the overall accuracy using the proposed ensemble learning technique.INDEX TERMS deep neural networks, vehicle make and model recognition, ensemble learning, and k-fold cross-validation

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“…This system might considerably decrease the number of efforts required to the calculation of cancer in medical practice when minimizing the amount of false positives which result in discomforting and unnecessary biopsies. CAD system about mammography might tackle 2 distinct processes: diagnosis of detected lesions (CADx) and detection of suspected lesion in a mammogram (CADe), viz., classifications as malignant/benign [4]. In recent years, Deep learning (DL) is considered an advanced technique since it has shown performances beyond the advanced in several machine learning tasks includes object classification and detection.…”

Section: Introductionmentioning

confidence: 99%

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Jos�Escorcia-Gutierrez¹,

Mansour²,

Bele駉³

et al. 2022

Computers, Materials &Amp; Continua

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Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

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Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model

Cited by 28 publications

References 21 publications

Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

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