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5th International Conference on Video, Signal and image processing, VSIP 2023

作者： Jin, Hongmei Cui, Kaihua Jia, Ni Zhang, Hao Shi, Yuying Xi'an University Of Science And Technology China

ISBN: (纸本)9798400709272

In order to solve the problem that existing fatigue driving detection methods have high model complexity and are difficult to deploy to embedded devices, this paper designs and implements a deep learning-based fatigue driving detection experimental system. The system integrates YOLOv5 fatigue detection algorithm and Zero-DCE low-light enhancement algorithm, and is deployed on an embedded development board. Firstly, the requirements of the system are analyzed to clarify the functional requirements of the system;secondly, the architecture, functional modules and interfaces of the system are outlined, and the development environment and interface of the fatigue driving detection terminal and the backend management system are designed in detail;finally, the development and implementation of the software system is completed, and the functionality of the system is tested. The system can detect driver fatigue status in real time and, establish visualization of big data Kanban analysis, real-time dynamic analysis of all kinds of fatigue abnormal information to issue timely alarms. © 2023 ACM.

关键词： deep learning

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Single image Dehazing Using A Tiramisu Auto- Encoder

Single Image Dehazing Using A Tiramisu Auto- Encoder

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ray, Ahan Sharanya, S. SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350375237

This paper presents a comprehensive approach to address the challenge of dehazing on-road images by synthesising datasets and training advanced deep learning models. Leveraging Pix2Pix GAN and introducing a novel Tiramisu Autoencoder architecture, the research endeavours to overcome the scarcity of real-world data through data augmentation and synthesis. The Pix2Pix GAN is modified to generate realistic haze, while the Tiramisu Autoencoder is used to de-haze the images. Challenges in data collection, including the absence of on-road data and time-series data, are elucidated. The novel architecture demonstrates promising results on benchmark datasets. The research boasts advances in both data synthesis and image de-hazing. © 2024 IEEE.

关键词： Roads and streets

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deep learning Gesture Recognition System Based on 3D Convolution 9

Deep Learning Gesture Recognition System Based on 3D Convolu...

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9th International Conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Long, Ken Li, Zhiyuan Xie, Binghong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9798350376548

The rapid development of the Internet of Things has promoted the progress of human-computer interaction technology, in which gesture recognition, as a key component, provides diversified applications for smart homes, elderly care, and other fields. In recent years, with the wide application of Wi-Fi, gesture recognition based on channel state information (CSI) has attracted much attention due to its advantages of flexibility, convenience, and cost-effectiveness. In this paper, we propose a gesture recognition system that includes an image conversion module and a 3D convolutional network recognition module. In the image conversion module, we treat the CSI subcarrier information from multiple receivers as a one-dimensional time series and convert them into images separately. Then by aggregating all the image data on the new depth dimension, we finally input the processed data into the 3D Convolutional network recognition module for gesture recognition. This module focuses on capturing changes in CSI information over time and space. We evaluated our model on a Widar3.0 open data set, and the experimental results show that the proposed method has a good effect in realizing cross-domain gesture recognition. © 2024 IEEE.

关键词： Wi-Fi

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Toward a Hardware Implementation of Lidar-based real-time Insect Detection

Toward a Hardware Implementation of Lidar-based Real-time In...

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Conference on real-time image processing and deep learning

作者： Vannoy, Trevor C. Rehbein, Elizabeth M. Logan, Riley D. Shaw, Joseph A. Whitaker, Bradley M. Montana State Univ Elect & Comp Engn Bozeman MT 59718 USA Montana State Univ Opt Technol Ctr Bozeman MT 59718 USA

ISBN: (纸本)9781510650817;9781510650800

real-time monitoring of insects has important applications in entomology, such as managing agricultural pests and monitoring species populations-which are rapidly declining. However, most monitoring methods are labor intensive, invasive, and not automated. Lidar-based methods are a promising, non-invasive alternative, and have been used in recent years for various insect detection and classification studies. In a previous study, we used supervised machine learning to detect insects in lidar images that were collected near Hyalite Creek in Bozeman, Montana. Although the classifiers we tested successfully detected insects, the analysis was performed offline on a laptop computer. For the analysis to be useful in real-time settings, the computing system needs to be an embedded system capable of computing results in real-time. In this paper, we present work-in-progress towards implementing our software routines in hardware on a field programmable gate array.

关键词： real-time classification insects lidar machine learning field programmable gate arrays

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Analysis of deep Feature Matching Algorithms in UAV Visual Localization

Analysis of Deep Feature Matching Algorithms in UAV Visual L...

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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Prutyanov, Viktor V. Ternov, Mikhail A. Kostrov, Daniil S. Hse University Moscow Russia

The work presents an analysis of the application of deep learning-based methods for the keypoint extraction and matching in the context of map-aided UAV visual localization. A method for visual localization in three degrees of freedom is proposed, which employs pretrained SuperPoint and LightGlue networks for both short-term optical flow and global frame-to-map matching components. The ability of SuperPoint and LightGlue to refine a prediction of a less accurate location estimator without fine-tuning was estimated. The accuracy of the proposed method was evaluated and a detailed analysis of keypoint-based image matching algorithms was conducted on the AdM-UAV dataset. The performance evaluation of the proposed method was carried out on NVIDIA Jetson Orin Nano and shows acceptable results for real-time visual localization on this hardware platform. © 2024 IEEE.

关键词： Optical flows

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Towards Automated Visual Inspection of Electrical Grid Assets for the Smart Grid - An Application to HV Insulators

Towards Automated Visual Inspection of Electrical Grid Asset...

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2024 International Conference on Communications Control and Computing Technologies for Smart Grids

作者： Lopes, Fernando Rocha, Pedro Coelho, Andre Polytech Univ Coimbra RuaMisericerdiaLagar CorticosS. Martinho Bispo P-3045093 Coimbra Portugal Inst TelecomunicaOes Pinhal Marrocos Polo II DEEC P-3030290 Coimbra Portugal EDP Lab Inspecdo Ativos P-2685038 Sacavem Portugal

ISBN: (纸本)9798350318562;9798350318555

The globally growing demand for electric power, projected to double by 2050, requires extensive upgrades in Transport and Distribution (T&D) systems. Despite the need for new infrastructures to address this demand, the reliability of ageing electrical T&D systems remains a more critical concern, where power line insulators account for over 50% of T&D maintenance costs. To boost inspection efficiency, electric utilities are integrating remote inspection technologies into their operations. However, the processing of the growing volume of collected data is currently strongly limited by human interpretation tasks. This research evaluates the performance of computer vision based on deep learning models for automated visual inspection of electrical grid assets, validated with real-world data. The developed work presents an example application of automated visual inspection of HV insulators, detecting defects in disc insulators in visible light images, using two state-of-the-art deep learning models. YOLOv8s achieved a mAP@50 of 87.9%, while Faster R-CNN X101-FPN achieved 87.2% for the same metric. The findings highlight the advantages and limitations of automated visual inspection, enabling utility companies to benefit from higher efficiency inspection processes, reducing the costs and improving the reliability of electrical grid maintenance. Assessing the performance and complexity of data-driven automated visual inspection techniques is crucial for developing streamlined models that effectively handle high data volumes, and that can evolve to real-time operation and integration in the monitoring and control functions of the smart grid or as a dynamic component of a digital twin of the grid.

关键词： Asset Inspection HV Insulator Computer Vision deep learning Smart Grid

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A Survey on Skin Lesion Detection and Classification using Machine learning 2

A Survey on Skin Lesion Detection and Classification using M...

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2nd International Conference on Artificial Intelligence and Machine learning Applications, AIMLA 2024

作者： Yadav, Rashmi Bhat, Aruna Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350349221

The research explores how dermatologists use machine learning to quickly and accurately identify and classify skin injury. Conventional diagnosis techniques depend on visual examination, but are subjective and have different interpretations. The ability to analyze data and recognize patterns is a possible remedy for ML. The paper examines the current technology of machine learning, focusing on validation in the real world, and deals with data set variability. Although the research acknowledges the fruitfulness of deep learning (DL), the research emphasizes the benefits of traditional ML techniques regarding interpretation and processing performance. Methods for automating the analysis of skin lesions, such as feature engineering, rule-based techniques and traditional ML algorithms, have been studied. The study suggests using advanced transfer learning techniques, integrating genetic and clinical data, and refining the way artificial intelligence (AI) is explained in order to get over barriers. Intending to enhance the accessibility and correctness of skin lesion identification, the future requires collaboration between dermatology and machine learning to develop real-time diagnostic tools. By offering scalable solutions for rapid diagnosis of lesions and improved patient outcomes, this combination of medical expertise and ML capabilities has the power to revolutionize dermatology. The future of automated dermatological diagnosis is expected to be shaped by collaboration between machine learning experts and dermatologists, enabling more personalized treatment for patients. © 2024 IEEE.

关键词： Dermatology

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An Intermediate deep Feature Fusion Approach for Understanding Human Activities from image Sequences

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SN Computer Science 2024年第8期5卷 1037页

作者： Garg, Aishvarya Nigam, Swati Singh, Rajiv Department of Physical Science Banasthali Vidyapith Rajasthan 304022 India Department of Computer Science Banasthali Vidyapith Rajasthan 304022 India Centre for Artificial Intelligence Banasthali Vidyapith Rajasthan 304022 India

Human activity recognition (HAR) is complex in real time because of varying views, illuminations, backgrounds, and colors. With the current state of the art, deep learning (DL) algorithms are gaining more attention because of their automated feature extraction in contrast to the handcrafted machine learning (ML) methods. In this work, we aim to exploit a data fusion approach for HAR and propose an intermediate feature fusion approach for vision-based HAR employing convolutional neural networks (CNN2D) and transfer learning (TL) techniques with pretrained residual neural networks (ResNet50) for the extraction of local and global features, respectively. These extracted features are then fused employing a concatenation layer before classifying activities. We have focused on detecting two categories of activities: action (single person) and interactions (human–human and human-object). The proposed activity recognition is able to detect human activities in constrained as well as unconstrained environments with multiple viewpoints. The proposed work is evaluated with five benchmark vision datasets, namely, KTH, Weizmann, IXMAS, CASIA action database, and MSR Daily Activity 3D, in terms of accuracy and confusion matrix. This proposed framework is able to recognize complex activities with better accuracy than single-person-based activities seen in the MSR Daily Activity 3D and CASIA datasets, gaining the highest accuracy of 99.94% and 99.76%, respectively. The comparative analysis with the existing state-of-the-art methods shows the superiority of the performance of the proposed model in terms of accuracy. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CNN deep learning Feature fusion Human activity recognition ResNet50 Transfer learning

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Aerial Intelligence for Precision Monitoring and Fault Identification in Large-Scale Solar Farms Using Advanced Drone Technology and deep learning Technique 15

Aerial Intelligence for Precision Monitoring and Fault Ident...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Moorthy, Ponkarthika Gupta, Akshaya Rajendra Chinnadurai, Kannika Parameshwari Mustafa, Ameena Banu Arunachalam, Sivakumar Kumar, Gokul Senthil Gupta, Mirudhubasnee Rajendra Kongu Engineering College Department of Electronics and Communication Engineering Erode India Npr College of Engineering and Technology Department of Electronics and Communication Engineering Dindigul District Natham India Nandha Engineering College Department of Civil Engineering Erode India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350370249

Solar energy is a promising source of renewable energy, and large-scale solar farms are becoming increasingly popular. However, the maintenance and monitoring of these farms can be challenging due to their size and the distributed nature of the solar panels. Traditional methods of inspection and maintenance are time-consuming, labor-intensive, and prone to human error. This project proposes an innovative solution for precision monitoring and fault identification in large-scale solar farms using advanced drone technology and image processing techniques. The methodology involves collecting high-resolution thermal images of solar panels using a drone equipped with a thermal camera. The YOLOv5 object detection algorithm is then used to detect and classify diode issues, Multi-cell hotspots, and hotspots in thermal images with high precision and recall. The proposed methodology consists of five major stages: data collection, model development, model training, model validation, and model testing. The model is trained and validated using a labeled dataset and then tested to automate the process of detecting and classifying defects or issues in real time. The use of drones and thermal imaging enables non-destructive and contactless inspection, reducing the risk of damage to the solar panels. Overall, this project demonstrates the potential of using advanced drone technology and deep learning algorithms for precision monitoring and fault identification in large-scale solar farms, contributing to the development of more sustainable and efficient solar energy systems. © 2024 IEEE.

关键词： labeled dataset object detection algorithm thermal camera

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A Comparative Analysis of CNN-based deep learning Models for Landslide Detection

A Comparative Analysis of CNN-based Deep Learning Models for...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Oak, Omkar Nazre, Rukmini Naigaonkar, Soham Sawant, Suraj Vaidya, Himadri Department of Computer Science & Engineering COEP Tech University Pune India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798350374933

Landslides inflict substantial societal and economic damage, underscoring their global significance as recurrent and destructive natural disasters. Recent landslides in northern parts of India and Nepal have caused significant disruption, damaging infrastructure and posing threats to local communities. Convolutional Neural Networks (CNNs), a type of deep learning technique, have shown remarkable success in image processing. Because of their sophisticated architectures, advanced CNN-based models perform better in landslide detection than conventional algorithms. The purpose of this work is to investigate CNNs’ potential in more detail, with an emphasis on comparison of CNN based models for better landslide detection. We compared four traditional semantic segmentation models (U-Net, LinkNet, PSPNet, and FPN) and utilized the ResNet50 backbone encoder to implement them. Moreover, we have experimented with the hyperparameters such as learning rates, batch sizes, and regularization techniques to fine-tune the models. We have computed the confusion matrix for each model and used performance metrics including precision, recall and f1-score to evaluate and compare the deep learning models. According to the experimental results, LinkNet gave the best results among the four models having an Accuracy of 97.49% and a F1-score of 85.7% (with 84.49% precision, 87.07% recall). We have also presented a comprehensive comparison of all pixel-wise confusion matrix results and the time taken to train each model. © 2024 IEEE.

关键词： Semantic Segmentation

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