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Indoor Smart Walker Based on Artificial vision for the Autonomous Movement of Seniors with Mobility Problems and Reduced vision 2nd

Indoor Smart Walker Based on Artificial Vision for the Auton...

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2nd international conference on Smart Technologies, Systems and Applications (SmartTech-IC)

作者： Guamushig-Laica, Jose Jerez-Gavilanez, Miguel Mendoza-Chipantasi, Dario Marino-Salguero, Jessica Univ Fuerzas Armadas ESPE Quijano & Ordonez Latacunga 050102 Ecuador Tech Univ Darmstadt Dolivostr 15 D-64293 Darmstadt Germany

ISBN: (纸本)9783030991708;9783030991692

The usual solution to mobility problems for the elderly lies in the use of conventional canes and walkers. However, when the older adult also has vision problems, these devices are not sufficient to provide safe walking, and the senior suffers from constant falls. This article proposes a smart walker based on artificial vision for indoor use in nursing homes that safely enables autonomous mobility for visually impaired seniors. The walker has been equipped with activation switches on the handlebars, ultrasonic sensors, a Kinect visual sensor, traction motors, and a controller. The control algorithm is based on reactive navigation, odometry and artificial vision and is designed to avoid obstacles and detect dangerous situations such as stairs. The control unit is a Raspberry Pi running Raspbian and relies on message passing between processes and visualization. The prototype of the smart walker is implemented in the nursing home "Luis Maldonado Tamayo" (Pujili-Ecuador), where tests are being carried out with elderly persons who still have motor skills and some partial vision impairment. The walker can effectively detect obstacles in a distance range from 50 - 86 cm. Almost 90% of small, 96% medium, and 98% dynamics obstacles are avoided. The demonstration video is available at https://***/MJzvzIily4k.

关键词： Intelligent walker Senior - mobility Machine vision image processing Mobile robotics

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A Novel Method for Pulmonary Artery Segmentation from CTPA images

A Novel Method for Pulmonary Artery Segmentation from CTPA I...

引用

Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Xin Guo Pengjuan Zhang Yaoyuan Guo Yan Wen School of Mathematics and Computer Engineering Ordos Institute of Technology Ordos China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Pulmonary artery (PA) segmentation is an important step in the diagnosis and treatment of pulmonary diseases. In the present work, a novel method for PA segmentation from CTPA images is proposed. First, the mediastinum is identified before the 2D PAs in the mediastinum are recognized through edge tracing. To detect PA images outside the mediastinum, the region growing technique is employed to obtain the 3D PA images. As the 3D segmentation results inside the mediastinum often suffer noises, the segmentation results inside the mediastinum are removed based on the identified 2D mediastinum region, with the PA target images outside the mediastinum retained. Last, the 2D PA images inside and outside the mediastinum are merged to obtain complete 2D imaging of PAs. Experiments show that the proposed method can achieve accurate segmentation of pulmonary arteries based on the characteristics of the arteries inside and outside the mediastinum.

关键词： image segmentation Three-dimensional displays Accuracy image edge detection Pulmonary diseases Noise Lung Object segmentation Medical services Arteries

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Cataract Disease Detection Based on Small Fundus images Dataset Using CNNs

Cataract Disease Detection Based on Small Fundus Images Data...

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computer Graphics and image processing (CGIP), international conference on

作者： Zaidatul Nisa'Binti Abdul Basit Yoshihiro Mitani University Teknologi MARA Selangor Malaysia National Institute of Technology Ube College Ube Japan

ISBN: (数字)9798350374186

ISBN: (纸本)9798350374193

Cataracts are a significant global health concern, particularly among the elderly population. A cataract is an eye condition characterized by the accumulation of fluid in the lens, resulting in cloudiness and blurred vision. Therefore, the development of computer-aided diagnosis (CAD) systems is required to classify cataracts. In this paper, we proposed a convolutional neural network (CNN) model and Visual Geometry Group (VGG 19) for cataract detection in the eyes. Cataract disease detection is framed as a binary classification problem: normal and cataracts. This study explored the generalization performance of the CNN and VGG 19 for cataract classification, considering the challenge of overtraining with a limited number of training samples. This study presented the effectiveness of CNNs and VGG19 models in the context of cataract detection by utilizing a variety of approaches, including image augmentation, finetuning strategies, and various image resolutions. The experimental results demonstrated that the augmented VGG19 model slightly outperformed both the unaugmented VGG19 model (92.72%) and the unaugmented CNN (89.14%) in terms of average accuracy, particularly at a fine-tuning level of -2 with an accuracy of 93.57%.

关键词： Cataracts Solid modeling Analytical models Adaptation models Visualization image resolution Computational modeling

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High-Accuracy Classification of Eye Diseases with Pre-Trained Deep Neural Networks

High-Accuracy Classification of Eye Diseases with Pre-Traine...

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Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Feng Zhu Kailin Wu school of computer Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

With the development of artificial intelligence technology, high-accuracy classification of eye diseases based on retinal images has become a hot research field. The methods section introduces the diagnosis and classification procedure of eye diseases based on deep neural networks. The experimental section first compared the classification performance of 9 different pre-trained deep neural networks, and then, discussed the impact of different data augmentation strategies on the classification results. Furthermore, the paper chose EfficientN et- Boas the benchmark model for classification, and compared the effects of different hyperparameters such as learning rates, batch size, and combination optimizer on the improved model's classification performance. The optimal hyperparameter setting for high-accuracy classification was obtained. The research results of the paper indicate that deep learning algorithms can achieve accurate diagnosis of eye diseases, which is of great significance for early clinical diagnosis in ophthalmology.

关键词： Deep learning Analytical models Machine vision Artificial neural networks Eye diseases Retina Robustness Classification algorithms Ophthalmology Tuning

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Covid-19 face mask detection using convolutional neural network and image processing 2

Covid-19 face mask detection using convolutional neural netw...

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2nd international conference for Emerging Technology, INCET 2021

作者： Mengistie, Tajebe Tsega Kumar, Deepak National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong India

ISBN: (纸本)9781728170299

Object detection and image classifications are the popular research work in the rapid growth of advanced technology to detect and identify real-time problems in the governmental core-areas such as business shops, airlines, universities, and army place with the help of webcams surveillance cameras, and opensource platforms. The objective of this research work is to propose Convolutional Neural Network (CNN) and Open Source computer vision (OpenCV) to detect COVID-19 face mask detection from real-time (live streaming video) and image datasets. We have employed data argumentation pre-processing techniques to maximize our dataset and improved the performance of the proposed models, and in addition to this, we have used MobileNet CNN architecture to reducing the size and model complexity. This research work's experimental result has been shown 96.50%, 97.07%, 99.27% accuracies obtained with the help of CNN, CNndata augmentation, MobileNet2V, respectively. © 2021 IEEE.

关键词： Convolutional neural networks

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Application of Improved Hover-Net in Nuclear Segmentation and Classification of Histopathological images

Application of Improved Hover-Net in Nuclear Segmentation an...

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image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Pengyuan Zhang Weihao Jin Jiahong Li Jiao Tian College of Information Science and Technology Donghua University Shanghai China College of Science Donghua University Shanghai China College of Chemistry and Chemical Engineering Donghua University Shanghai China College of Mechanical Engineering Donghua University Shanghai China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In HE-stained images, nuclear segmentation and classification are critical tasks for disease diagnosis and prognosis, which is of great significance [1]. The high intraclass morphological variability and interclass nuclear similarity make it complicated to perform nuclear segmentation and classification tasks simultaneously on HE-stained images. We need to use advanced computer vision and deep learning technology, combined with professional knowledge, to solve these problems effectively. This paper proposes an innovative Hover-net model-STi Hover-net, aiming to improve cell nuclei segmentation and classification accuracy in histopathological images. Combined with deep learning technology, it effectively captures the subtle features and spatial distribution of the nucleus by expanding the sensing area of the nucleus. It has achieved excellent performance on several cancer-type data sets.

关键词： Deep learning image segmentation Pathology Accuracy Computational modeling Feature extraction Sensors Labeling Medical diagnosis Prognostics and health management

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A Unified Approach for Weed Detection in Arable Acreage Using RetinaNet Architecture

A Unified Approach for Weed Detection in Arable Acreage Usin...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： M S Jagadeesh Alen Sebastian Veliyathuparamban N K V Manasa Adarsh Suresh Menon B N Jagadesh D R Kumar Raja School of Computer Science and Engineering (SCOPE) VIT-AP University Amaravati India FTMK UTeM Melaka Malaysia

One of the main elements that could reduce agricultural productivity is weeds. With the evolution in computer vision technologies, Deep Learning integrated with image processing techniques has proven to be an effective tool for the detection of weed. This study analyzes the benefits of accurate weed detection, which can help farmers to reduce the use of pesticides and increase agricultural output. Also, this study reviews various techniques used in recent years and discusses about the datasets that are used to train these techniques and presents certain challenges faced by the existing weed detection techniques in various situations. Moreover, this research study emphasizes the possible application of drones or unmanned aircraft systems for capturing high-quality images of weeds in fields for enabling precise treatment of weed affected areas. This study employs the VGG-16 network based RetinaNet architecture to analyze RGB photos of weeds in agricultural fields, which can aid farmers in reducing pesticide usage and enhancing crop yields.

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Research on Intelligent Driving System of Electric Vehicle Path Planning Based on computer Deep Reinforcement Learning

Research on Intelligent Driving System of Electric Vehicle P...

引用

image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Ma Wei Hubei Industrial Polytechnic Shiyan Hubei China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

A human-machine sharing control method based on the automatic driving of electric vehicles is proposed. The system consists of steering system, braking system, vehicle acceleration system, PLC system and external environment sensing system. This paper uses binocular stereo vision and multithreading technology to realize the sense of the surrounding environment. Siemens S7200PLC is used as controller to realize automatic control and human-machine conversion. The goal of this project is the shortest path, the shortest waiting time for charging, the minimum additional energy consumption and the maximum battery life. A multi-dimensional path planning method based on path selection, battery discharge threshold and charge stop time is constructed with the restrictions of path selection, battery discharge threshold and charge stop time. The calculation results show that the proposed algorithm can provide the user with the optimal energy consumption and mileage. The designed low speed electric vehicle intelligent driving system control is relatively stable. The system is relatively inexpensive and reliable.

关键词： Energy consumption Human-machine systems Steering systems Electric vehicles Path planning Discharges (electric) Sensors Batteries Stereo vision Safety

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Automated tomato harvesting system using image processing methods 2

Automated tomato harvesting system using image processing me...

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2nd international conference on Conflict Management in Global Information Networks, CMiGiN 2022

作者： Oleshko, Tamara Kvashuk, Dmytro Odarchenko, Roman Kravets, Ruslan National Aviation University Liubomyra Huzara ave. 1 Kyiv03058 Ukraine Bundleslab KFT Váli utca 4. 4. em. 2 Budapest1117 Hungary Lviv Polytechnic National University S. Bandera 12 Lviv79013 Ukraine

The efficiency of farming is increasingly dependent on precision farming. This is due to significant competition, the emergence of new pests and bacteria that spoil the crop, environmental problems, and many other factors from which products lose their value. One of such factors is the timeliness of harvesting. This is especially true in greenhouse complexes, where harvesting occurs regardless of the season, regularly after the ripening of products. The ripening of tomatoes is quite unpredictable, so it is necessary to identify the ripening process to harvest in time. Machine vision can solve this problem by highlighting a separate spectrum of color, which is characteristic of already-ripe tomatoes. Therefore, the article proposes a method for identifying the processes of tomato ripening using image processing methods based on color detectors. The OpenCV library was used for software implementation. A Rasbberry Pi unicameral computer was used to solve this problem. © 2022 Copyright for this paper by its authors.

关键词： Harvesting

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Research on computer vision System for Intelligent Management of Football Stadium Based on Internet of Things

Research on Computer Vision System for Intelligent Managemen...

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Sensors, Electronics and computer Engineering (ICSECE), IEEE international conference on

作者： Yongxiang Zhang Wuchang Shouyi University Wuhan China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

This paper proposes an intelligent management computer vision system based on the Internet of Things for the special needs of football stadiums. The system integrates advanced image processing algorithms and computer system architecture, aiming to realize the functions of all-round monitoring, audience behavior analysis and security early warning inside and outside the football stadium. The core of the system includes a network of high-definition cameras, edge computing nodes and a central server. Cameras are responsible for capturing the environment inside and outside the venue in real time, and initial data processing is carried out through edge computing nodes to reduce data transmission latency. The central server is equipped with advanced image recognition algorithms, which can identify player movements, spectator density distribution, and abnormal behavior, such as trespassing, crowd gathering, etc., and issue instant alerts. In addition, the system also has automated ticket management, facility maintenance reminders and energy monitoring functions. The experimental results show that the system significantly improves the management efficiency and safety of football stadiums, especially during large-scale events, and can effectively prevent and respond to various emergencies. At the same time, through intelligent analysis of audience behavior, the system can also provide decision support for venue managers, optimize resource allocation and enhance the spectator experience.

关键词： computer vision Systems architecture Cameras Real-time systems Maintenance Servers Internet of Things Monitoring Sports Edge computing

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