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A review of traffic violation detection technology in reporting mechanism 2

A review of traffic violation detection technology in report...

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2nd international conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Alaydrusl, A. Putra, W.K. Nugroho, Y. Muhammad Surantha, N. Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Traffic Violation Detection system using radio frequency identification (RFID) has been applied to detect vehicles with limited power sources through RFID Tag. Camera sensors are also applied to identify a vehicle and its plate number through image and video processing known as computer vision. On the other hand, vehicular ad hoc network (VANET) to gain information about location, speed, and so on through the vehicle-to-vehicle (V2V) connection. Lastly, the internet-of-things that is backed-up by cloud computing helps to store various information from each of these technologies and process them to get results. Many researchers have proposed and developed algorithms or technology with astonishing experimental results of them. However, there has been no review of the integration and reporting mechanism that correlates them. There has also been no review about how to connect the information to the authority and violator. Therefore, the review was made to explain each technology's method with their experiment's results and issues. Moreover, the future challenge also had been given to further research. © Published under licence by IOP Publishing Ltd.

关键词： Vehicles

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Research on Haze Traffic Signal image processing Based on Retinex Algorithm

Research on Haze Traffic Signal Image Processing Based on Re...

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ICMLCA 2021;2nd international conference on Machine Learning and computer Application

作者： Lijuan Qin Xinyu He Tao Liu

The signal lamp is affected by the haze weather conditions, and the color of the signal lamp image taken will be disturbed to a certain extent. Before color threshold segmentation, it is necessary to restore and enhance the color of the signal lamp to a certain extent, so as to ensure the accuracy and real-time performance of color segmentation and subsequent recognition. Retinex algorithm is an image enhancement theory based on human vision system. In recent years, it has been widely studied and applied. In this paper, SSR, MSR and MSRCR algorithms are introduced in detail, and these three methods are applied to haze traffic signal image enhancement. The experimental results of image processing are given, and the images have been enhanced to different degrees, and the experimental results are good. Traffic signal processing and recognition is one of the key technologies and difficulties of intelligent vehicle unmanned driving, so the research of this paper has important practical application value.

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Diabetic Retinopathy Diagnosis based on Convolutional Neural Network 2

Diabetic Retinopathy Diagnosis based on Convolutional Neural...

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2nd international Virtual conference on Pure Science, 2IVCPS 2021

作者： Abed, Mohammed Hamzah Muhammed, Lamia Abed Noor Toman, Sarah Hussein Faculty of Computer Science and Information Technology University of Al-Qadisiyah Iraq Road and Transport Department Faculty of Engineering University of Al-Qadisiyah Iraq

Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, as a result, it can cause blindness. therefore, diagnosis of this disease especially in the early time can prevent its effect for a lot of patients. To achieve this diagnosis, eye retina must be examined continuously. Therefore, computer-aided tools can be used in the field based on computer vision techniques. Different works have been performed using various machine learning techniques. Convolutional Neural Network is one of the promise methods, so it was for Diabetic Retinopathy detection in this paper. Also, the proposed work contains visual enhancement in the pre-processing phase, then the CNN model is trained to be able for recognition and classification phase, to diagnosis the healthy and unhealthy retina image. Three public dataset DiaretDB0, DiaretDBl and DrimDB were used in practical testing. The implementation of this work based on Matlab-R2019a, deep learning toolbox and deep network designer to design the architecture of the convolutional neural network and train it. The results were evaluated to different metrics;accuracy is one of them. The best accuracy that was achieved: for DiaretDB0 is 100%, DiaretDB1 is 99.495% and DrimDB is 97.55%. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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vision-Based Lane Detection for Advanced Driver Assistance Systems 2nd

Vision-Based Lane Detection for Advanced Driver Assistance S...

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2nd international conference on Power Engineering, Computing and Control, PECCON 2019

作者： Dharoori, Rakesh Acharya Narayanan, Sathiya School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9789811572401

Lane lines play a key role in indicating traffic flow and directing vehicles;lane detection serves as a core component in most of the modern-day advanced driver assistance systems (ADASs). computer vision-based lane detection is an essential technology for self-driving cars. This paper proposes a lane detection system to detect lane lines in urban streets and highway roads under complex background. In order to nullify the distortions caused by the camera lenses, we generate a distortion model by calibrating images against a known object, and apply ageneralized filtering approach using Sobel operator (Canny edge detection) in HLS color space. A bird eye view of image is generated using perspective transformation. A special search strategy using sliding window algorithm is used to detect lane lines, and later, curve fitting is done using polynomial regression. Thus, the obtained lane detector is overlaid upon a video to fill the detected portion of the lane. Then, it is applied to the video to detect lane lines. The image processing pipeline is written in Python using OpenCV libraries, and video processing is done using MoviePy. In this paper, the system developed is tested by applying it on a video taken from a camera mounted over the car. The environment used to implement the system is Anaconda. The results obtained show that the proposed system for lane detection, self-calibration and vehicle offset estimation is effective, accurate for both straight and curved lanes and robust to challenging environments. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Advanced driver assistance systems

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Improved CTPN Based Attention Mechanism for Scene Text Detection

Improved CTPN Based Attention Mechanism for Scene Text Detec...

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Big Data, Artificial Intelligence and Risk Management (ICBAR), international conference on

作者： Qian Sun Zhongjun Xiao Peng Ji Faculty of Electronics Electrical and Control Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Faculty of Electronics Electrical and Control Qilu University of Technology (Shandong Academy of Sciences)

Text, which can be seen everywhere in the natural environment, is one of the main ways to convey and communicate messages among people. In recent years, text detection and recognition techniques in natural scene images have become a hot research topic in the fields of computer vision, natural language processing, and instant translation. Global contextual information is crucial for natural scene text detection. However, most existing approaches rely on convolutional neural networks, and direct access to global contextual information is challenging due to the local nature of convolutional operations. Inspired by Swin-Transformer which has powerful global modeling capability, this paper proposes an improved CTPN model with Swin-Transformer instead of VGG16 as the backbone network for natural scene text detection, and uses FPN feature pyramids for feature fusion of different sizes to improve detection accuracy. On the ICDAR2015 dataset, the improved CTPN model incorporating Swin-Transformer improves the accuracy, recall, and F1 values by 1.3%, 3.3%, and 2.4%, respectively, compared with the CTPN model. The experimental results show that the improved CTPN algorithm incorporating Swin-Transformer can effectively and accurately detect the text location in natural scenes, laying a solid foundation for later text recognition and lab sheet interpretation.

关键词： Deep learning computer vision image recognition Text recognition Big Data Feature extraction Solids

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Abundant Semantic Information Module for Object Detection

Abundant Semantic Information Module for Object Detection

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), international conference on

作者： Taida Wu Qishen Li Qiufeng Li Jun Gao School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement.

关键词： Location awareness Fuses High performance computing Semantics Neural networks Object detection Detectors

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Mapping performance comparison of 2D SLAM algorithms based on different sensor combinations 2

Mapping performance comparison of 2D SLAM algorithms based o...

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2nd international conference on computer vision and Data Mining, ICVDM 2021

作者： Qu, Pengtao Su, Chen Wu, Hang Xu, Xinxi Gao, Sheng Zhao, Xiuguo Institute of Medical Support Institute of Systems Engineering Academy of Military Sciences Tianjin300161 China

Currently, some existing SLAM algorithms were widely applied in the field of mobile robots. However, most SLAM algorithms were tested either by simulation or using offline bags storing messages, which ignored other contributing factors influencing mapping including the changes of position and speed of the robot during it autonomous movement. The aim of this study is to investigate the solution of constructing more accurate map with high quality sensors based on freely available SLAM algorithms. Comparisons and analyses of three 2D SLAM algorithms (i.e. Gmapping, Hector SLAM and Cartographer) available in ROS were conducted to map different environments in our work using different sensor combinations of a LIDAR, a Stereo camera and an IMU, respectively. The research showed that the Cartographer algorithm has advantage over two other algorithms on constructing well-defined and informative environmental maps with minimal errors. Also, the LIDAR scan is more suitable than the ZED scan as the input of mapping algorithms, and the Cartographer combining the LIDAR scan, ZED odometry and IMU is the best solution to map in all combinations. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Stereo image processing

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Face feature matching based on semantic Information 2

Face feature matching based on semantic Information

引用

2nd international conference on computer vision and Data Mining, ICVDM 2021

作者： Guo, Jianlan Chen, Yuqiang Li, Jiaming Xie, Jiabao Department of Computer Engineering Dongguan Polytechnic Guangdong Dongguan523808 China

Face feature matching problems have always been a very important research topic in the field of image processing. Face feature matching is always the most important part of common face recognition algorithms, but how to match features quickly and accurately is a quite essential and urgent problem to be solved. Therefore, we propose a face feature matching method which can effectively improve the matching efficiency and accuracy. For each feature point in the input image, face feature matching based on nearest neighbor ratio method requires global search for the best matching in the reference image. However, we find that such matching method can achieve better matching results, but it costs a lot of computing resources and time. Therefore, we use the feature vectors constructed by CNN convolution feature map to provide the local matching region based on semantic information for handcrafted descriptors after feature matching. Although two feature matching processes are used in this paper, the matching efficiency is improved several times compared with the traditional global search scheme. Experiments show that our proposed can be used as an important module of face recognition system to improve the accuracy of face recognition. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Face recognition

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Transfer Learning Approach for Plant Leaf Disease Detection Using CNN with Pre-Trained Feature Extraction Method Mobilnetv2 2

Transfer Learning Approach for Plant Leaf Disease Detection ...

引用

2nd international conference on Robotics, Electrical and Signal processing Techniques, ICREST 2021

作者： Moyazzoma, Raida Hossain, Md. Al Amin Anuz, Md. Hasanuzzaman Sattar, Abdus Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665415767

Economy of Bangladesh mostly depended on agriculture. As a small country, our population is over the limit. We are also a developing country also. For national GDP growth must be to increase the production. Every year a huge amount of agricultural production losses for the crop disease. As we know most of the farmers in our country are illiterate, have no proper knowledge about the disease, that's why they cannot manually detect the disease. I think if you properly detect the disease at the early stage we can solve the issue. We develop a model that can classify leaf disease. We focus on 5 major production crops in Bangladesh. Using computer vision technique our farmer will get the benefit. We use convolutional neural networks for classifying images. An algorithm cannot properly capture the features of existing data that's why we use a pre-trained feature extraction method that is MobileNetv2. MobileNetv2 is very useful for mobile devices. The research contains the proportions of validation accuracy of 90.38%. This approach resulted in the agriculture sector that will help a farmer to classify disease from harvest. The main goal of our model is to minimize the damage of suffering plants that can help to the growth of production. By solving this issue themselves farmers can also reduce cost. Our goal is that they can cure their crop at the right time. To achieve this goal we tend to think that we should develop a way to detect the leaf disease. We collect several kinds of cucumber leaves. And then any leave can be tasted by our model. By using our model we try to reduce the leaf disease. © 2021 IEEE.

关键词： image classification

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Expression of Concern for: computer vision-Based Detection and Recognition Model of Objects for any Autonomous Systems in Low Illumination Condition

Expression of Concern for: Computer Vision-Based Detection a...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： P. Ashok A. Kavinilavu S. Ananthi P. Suganthi E.U. Iniyan Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai Department of Electronics and Communication Engineering 5Prathyusha Engineering College Chennai

ISBN: (数字)9781665437899

ISBN: (纸本)9781665437905

The theories and methods it studies have received extensive attention in many disciplines and fields, promoting the development of artificial intelligence systems and expanding the possibilities of computer applications. image processing is an important field of pattern recognition methods. The widely used character recognition (OCR) is a typicalapplication of pattern recognition in image processing. The pattern recognition process can be seen as a mapping process from the sample space to the category space. If you divide an n-dimensional feature space with n features asparameters into different regions, each region corresponds to a class of pattern classes. Among them, feature selection and extraction is an important part of pattern recognition. Ifthe selected features can fully reflect the essential features of the class, the classifier is easier to design. According to the presence or absence of standard samples, pattern recognition can be divided into surveillance identification methods and non-supervisory identification methods. The supervisory identification method is to design a classifierunder the condition that the class to which the training sample belongs is known, and the method for recognizing the sample is performed by the classifier.

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