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Traffic Sign Recognition System Based on Improved YOLOV5 23

Traffic Sign Recognition System Based on Improved YOLOV5

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Proceedings of the 6th International conference on Information Technologies and Electrical Engineering

作者： Haoxuan Liu Wuhan University of Technology China

ISBN: (纸本)9798400708299

To address the technical shortcomings of current autonomous driving systems for traffic sign recognition, this paper proposes a traffic sign recognition system based on the improved YOLOv5 algorithm. the system uses the pytorch deep learning framework as the system substrate to ensure a larger focus on the effective features without increasing the computational power of the network, and the deep learning system can speed up the recognition speed and improve the accuracy of the recognition system during the system iteration. For the training process of the system, firstly, various traffic sign icons in different situations are selected for data set creation and image pre-processing; secondly, the recognition network of the recognition system is established by using the pytorch deep learning framework, and the initial network recognition effect is poor, only 20.7%; and then the database is imported for model training, and the recognition accuracy and recognition speed of the system are improved in the process of multiple iterations. the database is then imported and the model is trained to improve the recognition accuracy and speed of the system in several iterations. Finally, the recognition ability of the system is enhanced in various environments and the average recognition accuracy of not less than 80% is achieved.

关键词： computer vision Deep learning Mosaic Neural network YOLOv5s

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A Novel Technique to Classify Face Mask for Human Safety 6

A Novel Technique to Classify Face Mask for Human Safety

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6th International conference on image Information processing, ICIIP 2021

作者： Nagaraj, P. Phebe, Gunta Sherly Singh, Anupam Sreyas Institute of Engineering and Technology CSE Department Informatics SoCS UPES Dehradun India

ISBN: (纸本)9781665433617

computer vision learning is a major area of focus because to the growing prevalence of the globally epidemic COVID-19, which will benefit healthcare management by increasing wellness in the general population. During the event, recognizing little things is a really troublesome errand of PC vision, as it incorporates getting arrangement and finding things underneath of pictures. Instead of rivals, the most impressive feature was being able to tell whether something is a face or a veil. Regardless, those that spread the disease benefit from the YOLOv3 advancements. In respect to GPU performance, the implementation of YOLOv3, which involves face veil identification, has a good performance. though it is light on memory and appropriate with the current trend. For our face-cover photo, we got the same number of people who wear veils and who don't. Constant video information ended up as part of the assessment since it concluded over concerns including privacy, location, and permission. the findings of the trials indicate that in preparation for 4,000 children, typical misfortune levels are 0.0730. After the new mAP (My Autonomous Programming) scores from 4,000 ages have been reported: they have a rating of 0.96. this technique of representation accomplished facial cover recognition with a yield of 96% identification accuracy. © 2021 IEEE.

关键词： computer vision

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An encoder-decoder based deep architecture for visible to near infrared image transformation 21

An encoder-decoder based deep architecture for visible to ne...

引用

12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Aswatha, Shashaank M. Malladi, Sai Phani Kumar Mukherjee, Jayanta Philips Benguluru India IIT Kharagpur India

ISBN: (纸本)9781450391276

Near-infrared (NIR) band is also one of the exciting wavelength ranges, besides optical range, to the human visual perception system. the NIR band shares various attributes with visible spectrum range. NIR images have the potential for better representation of vegetation and certain details of a scene that are sometimes opaque to RGB wavelengths. In this paper, we propose a dual encoder-decoder based architecture with different depths for transforming RGB images to NIR images. this network handles scale variations in data and helps to reduce artifacts in the transformed image domain. Two different resolutions of the input image are subjected to feature extraction by a series of five consecutive convolution and ReLu layers. then, their corresponding aggregated features are assembled and up-sampled to regress over the ground truth NIR image. We consider two datasets with different resolutions and properties, EPFL and OMSIV, which provide corresponding RGB-NIR pairs of images of natural scenes. We train and test our network on both the datasets and evaluate its qualitative and quantitative performance. the results of our network in transforming RGB images to NIR images are found to be interesting which are demonstrated through various examples. © 2021 ACM.

关键词： Infrared devices

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MergeNet: Single high dynamic range image reconstruction method 21

MergeNet: Single high dynamic range image reconstruction met...

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6th International conference on Multimedia Systems and Signal processing, ICMSSP 2021

作者： Liang, Bin Weng, Dongdong Ju, Ruikang Feng, Lulu MRAD of Beijing Institute of Technology China MRAD of Beijing Institute of Technology AICFVE of Beijing Film Academy China

ISBN: (纸本)9781450390378

the high dynamic range (HDR) environment mapping could provide great dynamic range and irradiance contrast for improving the fidelity of computer graphics Rendering, however, the acquisition of HDR images is much harder for most scenarios. We propose the improved deep merger network (MergeNet) to reconstruct an HDR image from a single filtered low dynamic range (FLDR) image with the feature extraction ability of deep learning methods and the band transmission characteristics of optical filters. Qualitative and quantitative comparisons have been executed for our method and other similar ones with multiple evaluation indicators. Experimental results show that our MergeNet performs favorably against state-of-the-art HDR image reconstruction methods. © 2021 ACM.

关键词： Mapping

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image Synthesis of Warli Tribal Stick figures using Generative Adversarial Networks 6

Image Synthesis of Warli Tribal Stick figures using Generati...

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6th IEEE International conference on Computing, Communication and Automation, ICCCA 2021

作者： Das, Nataraj Kundu, Shaona Deb, Suman Nit Dept. of Cse Agartala India

ISBN: (纸本)9781665414739

With the advancement in deep learning methods, Generative Adversarial Neural Networks(GANs) have been successful in the application of computer vision, Natural Language processing, and medical. It is observed to have great research potential of using GAN for image Synthesis. GANs can generate meaningful samples from complex patters or distributions. We present a method to generate Asian Tribal human synthetic stick figures using GAN. With variety of applications the output are limited to low resolution and are unrealistic. the work includes image to image translation where the inputs are low resolution images and as the training progresses, addition of new layers lead to better or high resolution output. For this purpose, it was mandatory to train the generator and discriminator following the principles governing the concept of transfer learning and to fetch evidence of how the generated output resembles the original data set. We also extend additional visualizations of our approach and support our GAN model with images of our generated output. this piece of work includes customized data set as input, where the input images are constructed based on laws of coordinate geometry and spherical polar coordinates and final generated images to illustrate its resemblance with the actual input images. the generated images were quite efficient in faking the discriminator, contributing towards robustness, precision and viability of the stated architecture. © 2021 IEEE.

关键词： Generative adversarial networks

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computer vision Based Face Mask Recognition in Religious Mass Gatherings and COVID-19 Infection

Computer Vision Based Face Mask Recognition in Religious Mas...

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International conference on Engineering Technology and their Applications (IICETA)

作者： Hussein Ali Rasool Basma Mohammed Khaleel Zainab Abed Almoussawi Raed Khalid Kassem AL-Attabi Zahraa N. Abdulhussain Altoosi University College Najaf Iraq Computer Technology Engineering Department Al Rafidain University College Bagdad Iraq College of Islamic Science Ahl Al Bayt University karbala Iraq Medical Technical College Al-Farahidi University Baghdad Iraq The Islamic university Najaf Iraq National University of Science and Technology Dhi Qar Iraq

Religious mass gathering at religious places presents a great difficulty in crowd management, providing the necessities of basic amenities, and addressing their healthcare requirements. Certain ritual practices may increase the risk of transferring respiratory pathogens. the COVID19 pandemic was an unexpected healthcare crisis with impacts reflected in every sphere of human lives. To limit the spread of the virus, the World Health Organization (WHO) declared wearing face masks is imperative manner. But manual assessment, whether an individual wears face masks or not in a public place, will be a difficult task. the need to monitor people wearing face masks is required to construct an automatic method. this study introduces a computer vision based Face Mask Recognition in Religious Mass Gatherings and COVID-19 Infection, named the CVFMR-RMG technique. the presented CVFMR-RMG technique aims to recognize the faces with and without masks via CV and image processing techniques. Initially, the input images are pre-processed via the adaptive Weiner filtering (WF) model. In the presented CVFMR-RMG technique, the face detection process takes place using Faster Region Convolutional Neural Network (Faster RCNN) approach. For facemask detection and classification, the sparse stacked auto encoder (SSAE) technique was employed. To improve the facemask detection performance of the SSAE model, a modified artificial fish swarm algorithm (MAFSA) is used for hyperparameter tuning. the performance assessment of the CVFMR-RMG system can be tested using the facemask database. the simulation outcomes illustrate the improved performance of the CVFMR-RMG system over other models.

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Deformable deep networks for instance segmentation of overlapping multi page handwritten documents 21

Deformable deep networks for instance segmentation of overla...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Aitha, Sowmya Bollampalli, Sindhu Sarvadevabhatla, Ravi Kiran CVIT IIIT Hyderabad Hyderabad500032 India

ISBN: (纸本)9781450391276

Digitizing via scanning the physical artifact often forms the first primary step in preserving historical handwritten manuscripts. To maximally utilize scanner surface area and minimize manual labor, multiple manuscripts are usually scanned together into a scanned image. therefore, the first crucial task in manuscript content understanding is to ensure that each of the individual manuscripts within a scanned image can be isolated (segmented) on a per-instance basis. Existing deep network based approaches for manuscript layout understanding implicitly assume a single or two manuscripts per image. Since this assumption may be routinely violated, there is a need for a precursor system which extracts individual manuscripts before downstream processing. Another challenge is the highly curved and deformed boundaries of manuscripts, causing them to often overlap with each other. To tackle such challenges, we introduce a new document image dataset called IMMI (Indic Multi Manuscript images). To improve the efficiency of dataset and aid deep network training, we also propose an approach which generates synthetic images to augment sourced non-synthetic images. We conduct experiments using modified versions of existing document instance segmentation frameworks. the results demonstrate the efficacy of the new frameworks for the task. Overall, our contributions enable robust extraction of individual historical manuscript pages. this in turn, could potentially enable better performance on downstream tasks such as region-level instance segmentation within handwritten manuscripts and optical character recognition. © 2021 ACM.

关键词： image enhancement

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Multimodal Learning Data Intelligent Personalized Learning Resource Recommendation System for Web-Based Classrooms

Multimodal Learning Data Intelligent Personalized Learning R...

引用

IEEE International conference on Artificial Intelligence and computer Applications (ICAICA)

作者： Shan Wang Shandong Vocational College of Science and Technology Weifang China

ISBN: (数字)9798350377255

ISBN: (纸本)9798350377262

Traditional recommender systems are difficult to fully explore the complex correlation between multimodal data and cannot meet learners' personalized needs. To this end, this paper proposes an intelligent personalized learning resource recommendation system (MDRS) based on multimodal data, which achieves accurate recommendation of learning resources by integrating advanced technologies such as natural language processing, computer vision, and deep learning, and at the same time, by introducing a multimodal data processing mechanism and personalized knowledge graph construction. First, we introduce a multimodal feature embedding module for the model, which utilizes deep neural networks for feature extraction and embedding of data in different modalities, such as text, image, audio, and video, in order to capture the multidimensional characteristics of resources. Subsequently, through the cross-modal fusion and attention mechanism module, the model performs weighted fusion of multimodal features to strengthen the expression of features related to user learning needs. By dynamically adjusting the weights of each modality, the system is able to automatically focus on the features that are most important for learning resource recommendation and eliminate redundant information. this mechanism ensures the system's adaptive fusion of different modalities, allowing multimodal information to be deeply related at the feature level. Next, we design a personalized knowledge graph module to associate users' learning behaviors with resource knowledge points to provide background knowledge support for the recommendation process, which updates the knowledge graph in real time by tracking learners' learning paths, preferences, and knowledge mastery and optimizes the recommended content based on learners' dynamic needs. Finally, through the recommendation optimization module, the recommendation strategy is dynamically adjusted in combination with user feedback to enhance the learning

关键词： Adaptation models Computational modeling Knowledge graphs Learning (artificial intelligence) Feature extraction Data processing Data models Real-time systems Recommender systems Optimization

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An Improved Harris Corner Points Detection for Low-Light Scenes Based on Contrast Limited Adaptive Histogram Equalization

An Improved Harris Corner Points Detection for Low-Light Sce...

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International conference on Imaging, Signal processing and Communication (ICISPC)

作者： Jiawei Guo Jieming Ma Ángel F. García-Fernández Ji Ge Yungang Zhang School of Advanced Technology Xi’an Jiaotong-Liverpool University(XJTLU) Suzhou China University of Liverpool UK ARIES Universidad Nebrija Spain JITRI Micro-Nano Automation Institute Suzhou China School of Information Science Yunnan Normal University Kunming China

ISBN: (纸本)9781665454810

Corner points are commonly defined as the intersection of two edges, and the Harris algorithm, which performs corner point detection based on the grey value variation between a patch and its neighborhood, is commonly used in various computer vision tasks. For low-light images, Harris algorithm is affected because the details of the image become blurred by low contrast. this paper proposes an improved Harris algorithm, which is inspired by the Contrast Limited Adaptive Histogram Equalization (CLAHE). After extracting a patch of the target image, the gray value of the patch is adjusted based on the cumulative distribution function (CDF). As a result, the gray value of the patch becomes evenly distributed, and the variation of the gray value of the patch becomes sharper. the improved Harris algorithm has been compared with the original Harris algorithm on different images of low-light scenes. Experimental results show that the proposed algorithm can effectively detect corner points in low contrast regions, and the repeatability of corner points matching in the low-light regions is significantly improved.

关键词： Histograms computer vision image edge detection Signal processing algorithms Imaging Adaptive equalizers Signal processing

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Chart classification: An empirical comparative study of different learning models 21

Chart classification: An empirical comparative study of diff...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： thiyam, Jennil Singh, Sanasam Ranbir Bora, Prabin K. IIT Guwahati India

ISBN: (纸本)9781450391276

Charts are powerful tools for visualizing and comparing data. Representation of information through charts grows with time due to its easy and aesthetically attractive structure. With the increase in the number of documents with various chart types, chart classification has become an important task for downstream applications such as chart data recovery, chart replenishment, etc. though there have been various studies reported in the literature on chart classification using different classification methods, three of the important concerns are small dataset size, a small number of chart types, and inconsistencies in the performance reported in different studies. Motivated by the above concerns, this paper curates a large dataset of real chart images (110k samples) with a large number of chart types (24 charts types) and evaluates 21 different machine learning models. To the best of our knowledge, this is the largest (in sample size and chart types) real chart dataset reported in the literature to date. We further study - (i) the effect of dataset size on the classification model, (ii) the nature of chart noises and their influences on classification performance, and (iii) confusing chart pairs leading to misclassification. © 2021 ACM.

关键词： image classification

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