Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform

2021 ◽  
Author(s):  
Ruchi Gajjar ◽  
Nagendra Gajjar ◽  
Vaibhavkumar Jigneshkumar Thakor ◽  
Nikhilkumar Pareshbhai Patel ◽  
Stavan Ruparelia
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Plant Leaf ◽  
Embedded Platform ◽  
Detection And Identification ◽  
Real Time Detection

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

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
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.

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

2019 ◽  
Vol 9 (14) ◽  
pp. 2865 ◽  
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).

Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks

2020 ◽  
Author(s):  
Olav A. Norgard Rongved ◽  
Steven A. Hicks ◽  
Vajira Thambawita ◽  
Hakon K. Stensland ◽  
Evi Zouganeli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Real Time Detection

scholarly journals Object Detection and Identification

2021 ◽  
Vol 10 (3) ◽  
pp. 1611-1618
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Text To Speech ◽  
Final Model ◽  
Detection Systems ◽  
Detection And Identification ◽  
Main Motive ◽  
Computer Based

Object Detection systems have been growing in the last few years for various applications. Since the hardware can not detect the smallest objects. Many algorithms are used for object detection like Yolo, R-CNN, Fast R-CNN, Faster R-CNN, etc. object detection using YOLO is faster than other algorithms and the YOLO scans the whole image completely at one time. Object detection, which is based on Convolutional Neural Networks (CNNs) and it's based on classification and localization. An object is detected by extracting the features of an object like the color of the object, the texture of the object or shape, or some other features. Then based on these features, objects are classified into many classes and each class is assigned a label. When we subsequently provide an image to the model, it will output many objects it detects, the location of a bounding box that contains every object with their label and score indicates the confidence. Text-To-Speech (TTS) conversion is a computer-based systemthat requires for the label are converted text-to-speech. The main motive is that the smallest amount of objects can be detected object and labeling the object with voice for real-time object detection. The final model architecture proposed is more accurate and provides the fast result of object detection with voice as compared to previous researches

scholarly journals Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yange Li ◽  
Han Wei ◽  
Zheng Han ◽  
Jianling Huang ◽  
Weidong Wang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
Video Monitoring ◽  
Construction Site ◽  
Web Crawler ◽  
Complex Scene ◽  
Real Time Detection

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.

Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks

2019 ◽  
Vol 66 (11) ◽  
pp. 8760-8771 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Adel Gastli ◽  
Lazhar Ben-Brahim ◽  
Nasser Al-Emadi ◽  
Moncef Gabbouj
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Detection And Identification ◽  
Fault Detection And Identification
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