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Driver Fatigue Countermeasure System 4

Driver Fatigue Countermeasure System

引用

4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Gonuguntla, Ahalya Rani Charan, Vemula Sai Likhitha, Ventrapati Srivalli, Yeddu Shivdeep, Yarmally Saikiran, Veerbhadrapolla Dept.of Computer Science Engineering B V Raju Institute of Technology TelanganaMedak Narsapur India

ISBN: (纸本)9798350305258

One of the major contributing factors to traffic accidents is driver weariness and drowsiness. Automated driver sleepiness detection is a significant computer vision challenge since accidents involving fatigued drivers have resulted in millions of fatalities in recent years. A real-time driver sleepiness detection system is created to address this issue and is based on Facial Landmark Detection. A significant barrier still exists in accurately identifying driver fatigue and forecasting it in real-time scenarios. In the proposed method, drowsiness detection is done by the fusion of eye state recognition and yawn detection. this study describes a non-intrusive driver fatigue detection system that can identify upcoming driver drowsiness and send out timely warnings that could be helpful. the system relies on visual cue, here mouth and eye to characterize the driver's alertness *** detect the mouth and eyes from the full face region, the system uses a Haar cascade classifier function and is followed by extraction of mouth and eyes from images. It also uses image processing techniques to extract the driver's mouth and eyes to determine their states. the proposed model is built using Python language and Open CV. © 2023 IEEE.

关键词： Driver Fatigue Eye Aspect Ratio(EAR) Eye Detection Facial Landmarks Haar Cascade Classifier Mouth Aspect Ratio(MAR) Mouth Detection threshold value

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Application of Artificial Neural Networks inSatellite Imaging - A Systematic Review 4

Application of Artificial Neural Networks inSatellite Imagin...

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4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Sharif, Hanan Rehman, Faisal Rida, Amina Nouman Ali, Chaudhry Zeeshan Zulfiqar, Rana Akram, Salman Kirn, Hina Hussain, Ayaz Iftikhar, Razia Information Technology Lahore Leads University Department of Computer Science Lahore Pakistan Mehran University of Engineering and Technology Szab Department of Computer Science Khairpur Mir's Pakistan

ISBN: (纸本)9798350335316

Artificial Neural Networks (ANN) and deep learning have been instrumental in the advancement of technology around the world for about 50 years, but because of the high costs associated with developing an optimal training and testing dataset, researchers have had to deal with several issues such as segmentation of images with low spatial resolution, object recognition, classification, and their use in the processing of satellite images has yet to reach its full potential. this work includes a thorough assessment of a significant body of research literature as well as the most important publications released in the last decade. the IEEE digital library, Science Direct, and SCOPUS system database indexing repository were the key sources for the review after applying various criteria, 386 publications pertaining to the case study were discovered. With 30 of them, grounds for exclusion and inclusion are discussed in further detail. Finding an upward trend in the level of research done in recent years indicates that this artificial neural network technology is getting more and more popular and curious. © 2023 IEEE.

关键词： image segmentation

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Arbitrary-Shape Text Spotting Based on Global, Pixel and Sequence Semantics 4th

Arbitrary-Shape Text Spotting Based on Global, Pixel and S...

引用

4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Zhang, Chunhu Ibrayim, Mayire Hamdulla, Askar Deng, Qilin Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Xinjiang University School of Information Science and Engineering Urumqi China Xinjiang University College of Future Technology Urumqi China

ISBN: (纸本)9789819787012

the field of end-to-end text spotting has garnered significant interest in recent years, propelled by the revealed intrinsic synergies between scene text detection and recognition. While advancements have been made, the challenge of arbitrarily shaped scene text spotting persists. this paper introduces an innovative feature augmentation module that addresses the issues of limited receptive fields and weak representation typical of lightweight backbone networks, while also enhancing multi-scale information more effectively and reducing information loss during feature aggregation. Furthermore, to extract a richer set of backbone features, we propose a dual information attention mechanism that enables the backbone network to neuronally focus on salient information. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

引用

4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. the data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Study on image style transfer for graphic creation using VGG19 and Pix2Pix model 4

Study on image style transfer for graphic creation using VGG...

引用

4th international conference on computer Graphics, image, and Virtualization, ICCGIV 2024

作者： Min, Long Shao, Mingrui Liu, Mengwen Li, Xiaozhou Ma, Dengyue Li, Xuelin Liu, Jingjing Faculty of Light Industry Qilu University of Technology 3501 Daxue Road Jinan250300 China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of Technology Shandong Academy of Sciences 3501 Daxue Road Jinan250300 China Key Laboratory of JiNan Digital Twins and Intelligent Water Conservancy Jinan250013 China School of Art & Design Qilu University of Technology 3501 Daxue Road Jinan250300 China Practice Teaching Center of Art & Design Shandong University of Art & Design Jinan250300 China

ISBN: (纸本)9781510683242

In computer vision field, the style transfer technique is to synthesize the style of one image with the content features of another image to generate a new migrated image, thus creating a unique graphic art image. In this thesis, the image style transfers achieved based on the VGG19 network model and pre-trained Pix2Pix network model separately were investigated by the comparison method, and the COCO dataset was selected as the dataset. the experiments used the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) to evaluate the performance of the style transfer images. It can be found that the PSNR of the migrated image of the VGG19 network model is 12.6776 and the SSIM is 0.2981, while the PSNR of the migrated image of the Pix2Pix network model is 12.9153 and the SSIM is 0.3182. the result indicates that the migrated image generated by the Pix2Pix network model is better than that by the VGG19 with respect to the image quality features. © 2024 SPIE.

关键词： Feature extraction image processing Data modeling

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Maize Pests Identification Based on Improved YOLOv4-Tiny 5

Maize Pests Identification Based on Improved YOLOv4-Tiny

引用

5th international conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Lin, Haiying Zhang, Yuyue Zhang, YuKun Zhang, Dexue Shandong University of Science and Technology Qingdao10424 China Binzhou University Binzhou10449 China

ISBN: (纸本)9781510662520

Agricultural pest identification occupies a key position in agricultural economy and development. the accurate identification of pests is the premise of agricultural pest control. In recent years, image processing technology and deep learning technology have rapidly developed. Some researches have been applied to the field of insect recognition. thus, some insect recognition deep learning models with good recognition accuracy and speed have been established. However, there is still much room for improvement when they were applied to the insect monitoring system deployed in the field. Considering the target recognition accuracy and speed, this paper selects the target detection algorithm YOLOv4-Tiny as the base model for insect recognition. the major advances are the attention mechanism and Spatial Pyramidal Pooling (SPP) structure as shown in: applying Convolutional Block Attention Module (CBAM) reduce computation and number of parameters;adopting SPP structure multi-scale pooling of input feature layers which increases the perceptual field and improves the robustness of the model. the experimental results show that the improved YOLOv4-Tiny model can significantly enhance the insect recognition accuracy. © 2023 SPIE.

关键词： Deep learning

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FFDR: Design and implementation framework for face detection based on raspberry pi 12

FFDR: Design and implementation framework for face detection...

引用

12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Alhajim, Dhafer Akbarizadeh, Gholamreza Ansari-Asl, Karim Islamic University Technical Engineering College Department of Computer Technical Engineering Najaf54001 Iraq Shahid Chamran University of Ahvaz Faculty of Engineering Department of Electrical Engineering Ahvaz Iran Shahid Chamran University of Ahvaz Faculty of Engineering Department of Electrical Engineering Ahvaz Iran

ISBN: (纸本)9781665412162

In today's world, we are surrounded by data of many types, but the abundance of image and video data available offers the data set needed for face recognition technology to function. Face recognition is a critical component of security and surveillance systems that analyze visual data and millions of pictures. In this article, we investigated the possibility of combining standard face detection and identification techniques such as machine learning and deep learning with Raspberry Pi face detection since the Raspberry Pi makes the system cost-effective, easy to use, and improves performance. Furthermore, some images of a selected individual were shot with a camera and a python program in order to do face recognition. this paper proposes a facial recognition system that can detect faces from direct and indirect images. We call this system FFDR, which is characterized by high speed and accuracy in the diagnosis of faces because it uses the Raspberry Pi 4 and the latest libraries and advanced environments in the Python language. © 2022 IEEE.

关键词： Face recognition

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WSports-50: An image Dataset for Women’s Sport Action Classification 4th

WSports-50: An Image Dataset for Women’s Sport Action Class...

引用

4th international conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. the Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. then, these features are pooled from a collection of regular regions representing local discriminative information. the spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. the proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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A Non-extensive Entropy-Based Adaptive Multi-threshold image Segmentation Algorithm 5

A Non-extensive Entropy-Based Adaptive Multi-threshold Image...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Kou, Qiaoying Xiong, Jing Sun, Mingjie Ou, Congjie Huaqiao University College of Information Science and Engineering Xiamen China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

image segmentation plays important role in computer vision and image processing, and the threshold segmentation is known by its powerful and efficient performance. Entropy-based algorithm and variance-based algorithm are two main threshold segmentation algorithms. In present paper, non- extensive Tsallis entropy is adopted to multi-level image thresholding. the non-extensive parameter is determined by the gray-level histogram in a self-adaptive way, which indicates the long-range correlations among pixels in an image. the segmentation results show that the proposed algorithm is superior to the well-known Kapur multi-level thresholding and variance- based Otsu algorithm in terms of PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and uniformity measure. Furthermore, it is found that the superiority of the proposed algorithm becomes stable as the number of thresholds increases. © 2022 IEEE.

关键词： Entropy

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Revolutionizing Human–computer Interaction: Unraveling the Power of Deep Learning Convolutional Neural Networks in Face recognition 4th

Revolutionizing Human–Computer Interaction: Unraveling the ...

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4th international conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Baharum, Aslina Halamy, Suhaida Ismail, Rozita Abdul Rahim, Emelia Mat Noor, Noorsidi Aizuddin Deris, Farhana Diana Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Selangor Malaysia School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Sarawak Kota Samarahan Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Kajang Malaysia Faculty of Business Management Universiti Teknologi MARA Cawangan Sarawak Sarawak Kota Samarahan94300 Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (纸本)9789819729760

Face recognition, being one of the most effective applications of image analysis, has recently received a lot of attention due to the huge implication in human–computer interaction (HCI). As the availability and eligibility to detect a person’s facial features, face recognition technology has been used in biometric detection applications as uses certain aspects of a person’s physiology to identify them. In addition, Deep Learning, under the subset of machine learning, can solve various problems, especially in image processing and face recognition. the advancement and development of Deep Learning can also enhance the use of the Convolution Neural Network (CNN) as the predominant model in the field of face recognition. the paper discusses the systems based on CNN approaches and algorithms and provides a review of the CNN face recognition approach. Furthermore, each paper’s details, such as used datasets, techniques, architecture, and obtained findings, hence the application are fully summarized and analyzed. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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