Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

Visual examination of the workplace and in-time reminder to the failure of wearing a safety helmet is of particular importance to avoid injuries of workers at the construction site. Video monitoring systems provide a large amount of unstructured image data on-site for this purpose, however, requiring a computer vision-based automatic solution for real-time detection. Although a growing body of literature has developed many deep learning-based models to detect helmet for the traffic surveillance aspect, an appropriate solution for the industry application is less discussed in view of the complex scene on the construction site. In this regard, we develop a deep learning-based method for the real-time detection of a safety helmet at the construction site. The presented method uses the SSD-MobileNet algorithm that is based on convolutional neural networks. A dataset containing 3261 images of safety helmets collected from two sources, i.e., manual capture from the video monitoring system at the workplace and open images obtained using web crawler technology, is established and released to the public. The image set is divided into a training set, validation set, and test set, with a sampling ratio of nearly 8 : 1 : 1. The experiment results demonstrate that the presented deep learning-based model using the SSD-MobileNet algorithm is capable of detecting the unsafe operation of failure of wearing a helmet at the construction site, with satisfactory accuracy and efficiency.

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Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190114151255 ◽

2020 ◽

Vol 16 (5) ◽

pp. 584-591 ◽

Cited By ~ 1

Author(s):

Muhammad Hanif Ahmad Nizar ◽

Chow Khuen Chan ◽

Azira Khalil ◽

Ahmad Khairuddin Mohamed Yusof ◽

Khin Wee Lai

Keyword(s):

Neural Network ◽

Neural Networks ◽

Heart Disease ◽

Aortic Valve ◽

Real Time ◽

Convolutional Neural Networks ◽

Valvular Heart Disease ◽

Detection System ◽

Processing Unit ◽

Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

Journal of Scientific Research ◽

10.3329/jsr.v13i3.52332 ◽

2021 ◽

Vol 13 (3) ◽

pp. 809-820

Author(s):

V. Sowmya ◽

R. Radha

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Real Time ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Traffic Management ◽

Data Augmentation ◽

Vehicle Detection ◽

Heavy Vehicles ◽

Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

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Deep Learning Approach for high Energy efficient Real-Time Detection of Weeds in Organic Farming

2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) ◽

10.1109/i2mtc50364.2021.9459943 ◽

2021 ◽

Author(s):

Vitali Czymmek ◽

Clarissa Moller ◽

Leif O. Harders ◽

Stephan Hussmann

Keyword(s):

Deep Learning ◽

Real Time ◽

Organic Farming ◽

Energy Efficient ◽

High Energy ◽

Learning Approach ◽

Real Time Detection

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A deep learning approach for brain tumour detection system using convolutional neural networks

International Journal of Dynamical Systems and Differential Equations ◽

10.1504/ijdsde.2021.120046 ◽

2021 ◽

Vol 11 (5/6) ◽

pp. 514

Author(s):

T. Kalaiselvi ◽

S.T. Padmapriya ◽

P. Sriramakrishnan ◽

K. Somasundaram

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Brain Tumour ◽

Convolutional Neural Networks ◽

Detection System ◽

Learning Approach ◽

Tumour Detection

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Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

Applied Sciences ◽

10.3390/app9142865 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2865 ◽

Cited By ~ 4

Author(s):

Kyungmin Jo ◽

Yuna Choi ◽

Jaesoon Choi ◽

Jong Woo Chung

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Detection System ◽

Motion Vector ◽

Detection Algorithm ◽

Surgical Instruments ◽

The Mean ◽

Real Time Detection ◽

Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

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A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks

Frontiers in Plant Science ◽

10.3389/fpls.2020.00751 ◽

2020 ◽

Vol 11 ◽

Cited By ~ 1

Author(s):

Xiaoyue Xie ◽

Yuan Ma ◽

Bin Liu ◽

Jinrong He ◽

Shuqin Li ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Real Time ◽

Convolutional Neural Networks ◽

Grape Leaf

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PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing

Remote Sensing ◽

10.3390/rs12071092 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1092

Author(s):

David Browne ◽

Michael Giering ◽

Steven Prestwich

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Data Augmentation ◽

Recognition Task ◽

Scene Recognition ◽

Training Data ◽

Learning Approach ◽

Scene Classification

Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learning approach to tackle these problems. We use transfer learning to compensate for the lack of data, and data augmentation to tackle varying scale and orientation. To reduce network size, we use a novel unsupervised learning approach based on k-means clustering, applied to all parts of the network: most network reduction methods use computationally expensive supervised learning methods, and apply only to the convolutional or fully connected layers, but not both. In experiments, we set new standards in classification accuracy on four remote-sensing and two scene-recognition image datasets.

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Newspaper Article Classification using Machine Learning Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2753.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 872-877

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Machine Learning Techniques ◽

Learning Approach ◽

Training Time ◽

Time Prediction ◽

Learning Techniques ◽

Prediction Time ◽

Newspaper Articles

Newspaper articles offer us insights on several news. They can be one of many categories like sports, politics, Science and Technology etc. Text classification is a need of the day as large uncategorized data is the problem everywhere. Through this study, We intend to compare several algorithms along with data preprocessing approaches to classify the newspaper articles into their respective categories. Convolutional Neural Networks(CNN) is a deep learning approach which is currently a strong competitor to other classification algorithms like SVM, Naive Bayes and KNN. We hence intend to implement Convolutional Neural Networks - a deep learning approach to classify our newspaper articles, develop an understanding of all the algorithms implemented and compare their results. We also attempt to compare the training time, prediction time and accuracies of all the algorithms.

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Assistant robot through deep learning

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i1.pp1053-1062 ◽

2020 ◽

Vol 10 (1) ◽

pp. 1053

Author(s):

Robinson Jiménez-Moreno ◽

Javier Orlando Pinzón-Arenas ◽

César Giovany Pachón-Suescún

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Speech Recognition ◽

Real Time ◽

Convolutional Neural Networks ◽

Spanish Language ◽

Assistive Robotics ◽

The One

This article presents a work oriented to assistive robotics, where a scenario is established for a robot to reach a tool in the hand of a user, when they have verbally requested it by his name. For this, three convolutional neural networks are trained, one for recognition of a group of tools, which obtained an accuracy of 98% identifying the tools established for the application, that are scalpel, screwdriver and scissors; one for speech recognition, trained with the names of the tools in Spanish language, where its validation accuracy reach a 97.5% in the recognition of the words; and another for recognition of the user's hand, taking in consideration the classification of 2 gestures: Open and Closed hand, where a 96.25% accuracy was achieved. With those networks, tests in real time are performed, presenting results in the delivery of each tool with a 100% of accuracy, i.e. the robot was able to identify correctly what the user requested, recognize correctly each tool and deliver the one need when the user opened their hand, taking an average time of 45 seconds in the execution of the application.

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