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Classification of Diabetic Retinopathy Using image Pre-processing Techniques

Classification of Diabetic Retinopathy Using Image Pre-proce...

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Intelligent Technologies (CONIT), international conference on

作者： Kavya Duvvuri Harshitha Kanisettypalli Masabattula Teja Nikhil Suja Palaniswamy Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Most diseases that affect humans cause an effect on the eyes which is observed first by a physician while treating a patient. Eye diseases are conditions that affect the functioning of the eye and lead to loss of vision. Globally, at least 2.2 billion people have near or distant vision impairment. Most of these eye diseases have long-term effects so, early detection of the eye disease is very important to avoid the consequences. Some eye diseases include cataracts, glaucoma, diabetic retinopathy, and retinal detachment, etc. Almost 10% of people are facing an eye disease called diabetic retinopathy (DR). In this paper, multiclassification of DR images has been done after performing a few preprocessing techniques like erosion, histogram equalization, and comparing the accuracy of the CNN model before and after the pre-processing. The classification model yielded an accuracy of 86.4% on retinal fundus images filtered from the Kaggle DR detection database. The obtained results show that the proposed preprocessing methods are best suitable for diagnosing diabetic retinopathy from retinal fundus images.

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Deep Learning Approaches for Pneumonia Classification in Healthcare

Deep Learning Approaches for Pneumonia Classification in Hea...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Sriram G K Karunasagar A Sudharsan R A Malini Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai India

In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known imageNet computer vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of image classification was later extended to image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed.

关键词： Deep learning image segmentation Pulmonary diseases Computational modeling Object detection Medical services Classification algorithms

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A Method of Camera Link image Data processing Based on FPGA

A Method of Camera Link Image Data Processing Based on FPGA

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2018 3rd international conference on Computational Modeling, Simulation and Applied Mathematics (CMSAM 2018)

作者： Shi-feng WANG

Camera Link is based on the development of Channel Link Technology. The data is transmitted using low voltage differential signal(LVDS). It has some advantages, such as fast speed, strong anti-interference ability and so on. Camera Link is widely used in the field of robot vision applications. FPGA integrates rich gate circuit resources and has the advantages of high speed, which makes it suitable for processing parallel data. In this paper, a Camera Link image data processing method based on FPGA is proposed, which is verified by simulation and has been successfully applied to practical projects.

关键词： Camera Link image Data processing FPGA

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computer vision for Determining Trajectory and Impact Position of Fighter-Aircraft Bombing

Computer Vision for Determining Trajectory and Impact Positi...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), international conference on

作者： Ardian Infantono Ridi Ferdiana Rudy Hartanto Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia

The motion detection process of bombing by fighter aircraft or combat drones is very important in the Weapon Impact Scoring System (WISS). Recently, the available WISS results are only based on the impact position of the fighter-aircraft bomb in the Air Weapon Range (AWR). The combat training safety regulation pushes observation which can only be made away from AWR. This paper presents the technique of computer vision to enhance WISS capability in observing fighter-aircraft bombs in the AWR. In this proposed method, not only the impact position is observed, but also the trajectory of the bomb gliding before exploding on the ground will be captured. This technique uses a digital video camera safely placed far from the AWR. The camera with a certain altitude on the tower is directed to the circle target to the source of the bombing. The proposed method processes the five-points position of the target circle like a clock (12-3-6-9-central point) while observing the bombing process prior to impact in the AWR. Based on real-time video processing, the image processing successfully makes a sequence of bomb trajectory and its impact position quickly. The results are very important to inform the pilot to improve the next bombing and to support precise analysis of more comprehensive bombing training.

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Real-Time Detection of Vehicles on South Asian Roads 3rd

Real-Time Detection of Vehicles on South Asian Roads

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3rd international conference on Machine Learning, image processing, Network Security and Data Sciences, MIND 2021

作者： Pawar, Rutuparn Gujar, Shubham Chougule, Suyash Pote, Rutuja Pandit, Dipti Dandawate, Yogesh Department of Electronics and Telecommunication Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Mechanical Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Computer Science Vishwakarma Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789811958670

Great advances have been made in the field of computer vision especially for object detection in the last two decades. Vehicle detection is fundamentally a task of object detection which assists in traffic control and management via traffic flow analysis. Models trained for vehicle detection on popular datasets containing vehicles as classes perform poorly while detecting vehicles in South Asian countries since the make and model of vehicles in the geographical region is different from those present in most popular datasets. Furthermore, some kinds of vehicles are found only in South Asian countries. In this paper, we propose a method that uses two YOLO models for the detection of various kinds of vehicles found on roads in South Asian countries. The first YOLO model detects vehicles while the second YOLO model detects wheels in case a truck is detected to further classify a truck based on the number of axles. A dataset consisting of images of bicycles, motorcycles, cars, buses, trucks, tempos and rickshaws was collected from surveillance videos, web scraping and subsetting the Indian Driving Dataset (IDD) as well as the Common Objects in Context (COCO) dataset. Another dataset was made which consists of images of trucks with their wheels annotated. YOLOv4 models trained on the latter mentioned datasets performed better than YOLOv3 and achieved a mAP of 71.08 per cent and 94.33 per cent for detection of vehicles and wheels, respectively. The proposed method to determine the number of axles of a truck gave an overall accuracy of 95 per cent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trucks

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Harnessing CNNs for Accurate Skin Disease Identification

Harnessing CNNs for Accurate Skin Disease Identification

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Parveen Dhoundiyal Vikrant Sharma Satvik Vats Karan Singh Department of Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India School of Computer and System Sciences JNU New Delhi New Delhi India

Skin disorders are common and can be brought on by several things, including viruses, bacteria, allergies, or fungi. The speed and precision of detecting skin diseases have increased because of developments in laser and photonics-based medical technologies. The expense of such a diagnostic is still considerable, though, which restricts accessibility. An automated early-stage dermatological screening system has been developed using image processing techniques to solve this issue. Effective skin disease classification requires the extraction of key information from pictures, and many detection algorithms rely on computer vision. DenseNet169 and NASNetMobile are some of the methods that were employed in the study. Graphs were used to display the results as the researchers compared the algorithms' performance based on training accuracy and confusion matrix. Keywords: Skin Disease, Medical Diagnosis, Deep Learning, Transfer Learning, CNNs

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Design of Industrial Robot Sorting System with Visual Guidance Based on Webots

Design of Industrial Robot Sorting System with Visual Guidan...

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international conference on computers, Communications, and Systems (ICCCS)

作者： Yunlong Pan Xing Ma Chunyang Mu Haiping An Jianyu Chen College of Electrical and Information Engineering North Minzu University Yinchuan China Institute of Information and Communication North Minzu University Yinchuan China College of Mechatronic Engineering North Minzu University Yinchuan China

ISBN: (纸本)9781538663516

Aiming at the low efficiency of artificial detection in industrial production line, this paper builds a simulation platform of visual guide sorting system based on Webots. For static target work pieces, image is captured by the camera, use Hu invariant moments quick recognition, and get the help of chain code to further identify, so that the sorting task can be done by robots. For dynamic target work pieces, it takes much time to process the image. There will be a tracking lag phenomenon. Using Kalman filter to predict the position, we can track the target. Experimental results showed that, with the feature in the centroid of shape and the tracking error in two pixels or less, the system could achieve target-detecting and tracking at the 0.2Sm/s speed of conveyor, as well as have a better robustness.

关键词： Cameras Sorting Robot vision systems Service robots Target tracking Shape

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Object Sorting Automated System using Raspberry Pi

Object Sorting Automated System using Raspberry Pi

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international conference on Communication and Electronics Systems (ICCES)

作者： Sushrut Nagesh Kulkarni Sanjay Kumar Singh Department of Electronics and Communications Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781538647660

Typically sorting of items is done manually requiring human work. Identifying a particular object and placing it in the required order is a taxing work especially in the industrial field wherein one needs to sort a large number of objects in a small interval of time and also weight of the objects is much larger than what an average human can bear. Automation plays a noteworthy role in such cases. Taking mentioned factors under consideration alongside providing a cost efficient solution, we have presented our system in this paper. We are using Raspberry Pi, which is an open source board based on Linux. In today's technology, raspberry pi has been a key in a major number of applications in automation. Our objective will be to examine its utility and effective use as a mechanical system in Sorting of *** we are making use of a web camera that scans the image of the particular object to be sorted. The scanned image is then further processed using OpenCV to detect the shape and color of the object. OpenCV (Open Source computer vision) is a library of programming functions mainly aimed at real-time computer vision. With the help of the processed information in OpenCV, motor driver is instructed to control the servo motors which will help drive the object to the required sorted section. In our project, objects of 3 different colors and shapes, i.e. Pentagon, Square and Triangle with Red, Green and Blue colors.

关键词： image color analysis Shape Servomotors Sorting Robots Universal Serial Bus

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Prediction of solar radiation in Qinghai Lake area based on BilSTM-attention method 3

Prediction of solar radiation in Qinghai Lake area based on ...

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3rd international conference on Advances in image processing, ICAIP 2019

作者： Wang, Zhenye Ye, Chengxu Wang, Wentao Yang, Ping School of Computer Qinghai Normal University Qinghai Xining China School of Geography Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9781450376754

Short-term solar radiation prediction plays a crucial role in production and life. There is much room for improvement in the prediction accuracy and stability of traditional models. In order to solve this problem, this paper uses a method based on deep learning to predict solar radiation. A short-term solar radiation prediction model is established for Qinghai Lake combined with a bidirectional long-term memory network and attention mechanism. Based on the historical solar radiation in the past month and the average temperature at 1.5 meters, the solar radiation prediction in the next two weeks is made prediction. The experimental results show that the prediction model combined with the bidirectional long-term memory network and the attention mechanism is superior to the traditional prediction method in predicting accuracy, convergence speed and root mean square error and average absolute error, which can effectively improve. The accuracy and stability of the short-term solar radiation prediction model in local areas. © 2019 Association for Computing Machinery.

关键词： Solar radiation

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Research on Object Detection Algorithm Based on Improved Yolov5 3rd

Research on Object Detection Algorithm Based on Improved Yol...

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3rd international conference on Artificial Intelligence, 2022

作者： Chen, Shuping Chen, Bingcai School of Computer Science and Technology Xinjiang Normal University No. 102 Xinyi Road Xinjiang Urumqi830054 China School of Computer Science and Technology Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China

ISBN: (纸本)9789811694226

Object detection is an important fundamental problem in computer vision research, and is also the basis for other high-level visual tasks such as object tracking, behavioral analysis, and image description. Since target recognition is the simultaneous recognition and assessment of multiple targets, the accuracy of target recognition is generally low. In order to improve the accuracy of target detection, this paper introduces the Efficient Channel Attention (ECA) module to optimize the original yolov5 model, which has fewer parameters and significantly improved performance. After the ECA module performs channel-by-channel global average pooling without reducing the dimensionality, it considers each channel and its k neighbors to capture local cross-channel interactions, which enhances the discrimination of features. Experiments on the coco data set show that the improved yolov5 model based on the ECA module proposed in this paper can be used 68.84% accuracy to detect these object. Compared with the comparison algorithm, the training accuracy of the yolov5 model integrated with the ECA module is improved 3.02%, which proves that the ECA module can further improve the accuracy of yolov5, and can meet the requirements of reliability and stability of target detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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