Efficient mobilenet architecture as image recognition on mobile and embedded devices

Khasoggi, Barlian; Ermatita, Ermatita; Samsuryadi, Samsuryadi

doi:10.11591/ijeecs.v16.i1.pp389-394

Cited by 22 publications

(16 citation statements)

References 11 publications

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“…As opposed to MobileNet V2 [ 63 ], MobileNet [ 4 ] is a CNN-based model that is extensively used to classify images. The main advantage of using the MobileNet architecture is that the model needs comparatively less computational effort than the conventional CNN model that makes it suitable for working over mobile devices and the computers that work over lower computational capabilities [ 64 , 65 , 66 ]. The MobileNet model is a simplified structure that incorporates a convolution layer that can be used in distinguishing the detail that relies on two manageable features that switch among the parameter’s accuracy and latency effectively.…”

Section: Methodsmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

404

169

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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Section: Methodsmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

404

169

View full text Add to dashboard Cite

show abstract

“…In this study, three separate deep CNNs were used: Inception-v3, Resnet152, and Inception-Resnet-v2. For image recognition on mobile devices and embedded devices with limited resources and ARM-based CPUs, this study [13] uses the Convolutional Neural Networks (CNN) approach with a 28 layer MobileNet architecture and works with a moderate amount of training data. The architectures described above were studied to create the proposed architecture used in this research and to examine different model complexities.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Deep Learning models on UV ink : a Fake Money detection scheme with RPN

Chowdhury

Das

Karmaker

2021

Preprint

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As soon as coins or money was invented, there were people trying to make counterfeits. Counterfeit money is fake money that is produced without the permission of the state or government, usually to imitate the currency and deceive the intended recipient. In Bangladesh, this is a significant problem and the problem is becoming more and more phenomenon as the days are passing by. Today’s modern bank notes have several security features that makes easier to identify fake notes. One of the security features is the use of UV ink. Bank notes deliberately put random flecks of color scattered all over the surface of the money - which acts as a extra layer of protection against counterfeiters. We propose an automatic authentication model for identifying counterfeit money based on these random flecks of color which is visible under UV light. To obtain a benchmark result, existing object detection pre-trained models were used, followed by MobileNet, Inception, ResNet50, ResNet101, and Inception-ResNet architectures. After that, using the Region Proposal Network (RPN) method with Convolutional Neural Network (CNN) based classification the optimal model was proposed. The proposed model had a 96.3 percent accuracy. It is critical to reduce the circulation of counterfeit money in a country’s economy to stop inflation. This study will aid in the detection of counterfeit money and, hopefully, reduce its spread.

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“…This results in light deep neural networks. By defining the network in such simple terms, it is possible to easily explore its structure and outline a good network [25].…”

Section: Mobilenetmentioning

confidence: 99%

Fish Recognition Model for Fraud Prevention using Convolutional Neural Networks

Monteiro¹,

Ribeiro²,

Viana³

et al. 2021

Preprint

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Fraud, misidentification, and adulteration of food, whether unintentional or purposeful, are a worldwide and growing concern. Aquaculture and fisheries are recognized as one of the sectors most vulnerable to food fraud. Besides, a series of risks related to health and distrust between consumer and popular market that this sector develop an effective solution for fraud control. Species identification is an essential aspect to expose commercial fraud. Convolutional neural networks (CNNs) are one of the most powerful tools for image recognition and classification tasks. Thus, the objective of this study is to propose a model of recognition of fish species based on CNNs. The results obtained show an algorithm with an accuracy of 86%, providing an effective solution to prevent fish fraud.

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Efficient mobilenet architecture as image recognition on mobile and embedded devices

Cited by 22 publications

References 11 publications

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Evaluation of Deep Learning models on UV ink : a Fake Money detection scheme with RPN

Fish Recognition Model for Fraud Prevention using Convolutional Neural Networks

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