scholarly journals Flame Detection Using Appearance-Based Pre-Processing and Convolutional Neural Network

2021 ◽  
Vol 11 (11) ◽  
pp. 5138
Author(s):  
Jinkyu Ryu ◽  
Dongkurl Kwak
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Region Of Interest ◽  
Fire Detection ◽  
Corner Detection ◽  
Detection Algorithms ◽  
Harris Corner ◽  
Processing Step ◽  
Flame Detection ◽  
Color Conversion

It is important for fire detectors to operate quickly in the event of a fire, but existing conventional fire detectors sometimes do not work properly or there are problems where non-fire or false reporting occurs frequently. Therefore, in this study, HSV color conversion and Harris Corner Detection were used in the image pre-processing step to reduce the incidence of false detections. In addition, among the detected corners, the vicinity of the corner point facing the upper direction was extracted as a region of interest (ROI), and the fire was determined using a convolutional neural network (CNN). These methods were designed to detect the appearance of flames based on top-pointing properties, which resulted in higher accuracy and higher precision than when input images were still used in conventional object detection algorithms. This also reduced the false detection rate for non-fires, enabling high-precision fire detection.

scholarly journals Enhanced Convolutional-Neural-Network Architecture for Crop Classification

2021 ◽  
Vol 11 (9) ◽  
pp. 4292
Author(s):  
Mónica Y. Moreno-Revelo ◽  
Lorena Guachi-Guachi ◽  
Juan Bernardo Gómez-Mendoza ◽  
Javier Revelo-Fuelagán ◽  
Diego H. Peluffo-Ordóñez
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Classification Model ◽  
Classification Error ◽  
Small Scale ◽  
Post Processing ◽  
Average Accuracy ◽  
Processing Step ◽  
Crop Classification

Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.

scholarly journals Multi-Scale Convolutional Neural Network for Accurate Corneal Segmentation in Early Detection of Fungal Keratitis

2021 ◽  
Vol 7 (10) ◽  
pp. 850
Author(s):  
Veena Mayya ◽  
Sowmya Kamath Shevgoor ◽  
Uma Kulkarni ◽  
Manali Hazarika ◽  
Prabal Datta Barua ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Region Of Interest ◽  
Cost Effective ◽  
Fungal Keratitis ◽  
Contact Lens Wear ◽  
Multi Scale ◽  
Effective Interventions ◽  
Prolonged Contact ◽  
Segmented Images

Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.

Light-Weight Convolutional Neural Network For Fire Detection

2021 ◽  
Author(s):  
Mohamed Abdel-Zaher ◽  
Mustafa Hisham ◽  
Retaj Yousri ◽  
M. Saeed Darweesh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fire Detection ◽  
Light Weight

scholarly journals Reinforcement learning and convolutional neural network system for firefighting rescue robot

2018 ◽  
Vol 161 ◽  
pp. 03028 ◽  
Author(s):  
Tien Kun Yu ◽  
Yang Ming Chieh ◽  
Hooman Samani
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Region Of Interest ◽  
Infrared Sensors ◽  
Sliding Windows ◽  
Q Learning ◽  
Rescue Robot ◽  
Neural Network System ◽  
The Face ◽  
Color Sensors

In this paper, we combine the machine learning and neural network to build some modules for the fire rescue robot application. In our research, we build the robot legs module with Q-learning. We also finish the face detection with color sensors and infrared sensors. It is usual that image fusion is done when we want to use two kinds of sensors. Kalman filter is chosen to meet our requirement. After we finish some indispensable steps, we use sliding windows to choose our region of interest to make the system’s calculation lower. The least step is convolutional neural network. We design a seven layers neural network to find the face feature and distinguish it or not.

scholarly journals A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks

2020 ◽  
Vol 17 (22) ◽  
pp. 8447
Author(s):  
Dima M. Alalharith ◽  
Hajar M. Alharthi ◽  
Wejdan M. Alghamdi ◽  
Yasmine M. Alsenbel ◽  
Nida Aslam ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Periodontal Disease ◽  
Convolutional Neural Network ◽  
Region Of Interest ◽  
Training Dataset ◽  
Detection Accuracy ◽  
Detection Model ◽  
Art Object ◽  
Orthodontic Patients

Computer-based technologies play a central role in the dentistry field, as they present many methods for diagnosing and detecting various diseases, such as periodontitis. The current study aimed to develop and evaluate the state-of-the-art object detection and recognition techniques and deep learning algorithms for the automatic detection of periodontal disease in orthodontic patients using intraoral images. In this study, a total of 134 intraoral images were divided into a training dataset (n = 107 [80%]) and a test dataset (n = 27 [20%]). Two Faster Region-based Convolutional Neural Network (R-CNN) models using ResNet-50 Convolutional Neural Network (CNN) were developed. The first model detects the teeth to locate the region of interest (ROI), while the second model detects gingival inflammation. The detection accuracy, precision, recall, and mean average precision (mAP) were calculated to verify the significance of the proposed model. The teeth detection model achieved an accuracy, precision, recall, and mAP of 100 %, 100%, 51.85%, and 100%, respectively. The inflammation detection model achieved an accuracy, precision, recall, and mAP of 77.12%, 88.02%, 41.75%, and 68.19%, respectively. This study proved the viability of deep learning models for the detection and diagnosis of gingivitis in intraoral images. Hence, this highlights its potential usability in the field of dentistry and aiding in reducing the severity of periodontal disease globally through preemptive non-invasive diagnosis.

scholarly journals Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

2019 ◽  
Vol 46 (2) ◽  
pp. 746-755 ◽  
Author(s):  
Michal Byra ◽  
Michael Galperin ◽  
Haydee Ojeda‐Fournier ◽  
Linda Olson ◽  
Mary O'Boyle ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Deep Convolutional Neural Network ◽  
Breast Mass ◽  
Mass Classification ◽  
Color Conversion
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