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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13664 条记录，以下是1351-1360 订阅

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A Robust Road Lane Detection Using Computer Vision Approach for Autonomous Vehicles

A Robust Road Lane Detection Using Computer Vision Approach ...

引用

Advances in Data Engineering and Intelligent Computing systems (ADICS), International conference on

作者： R.M. Bhavadharini J. Sutha School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The quality of human life is improving over a period of time with the technological advancements in the arena of Artificial Intelligence and Internet of Things. Various fields adopt and implement automation process for eco-nomic growth. The recent advancement and transition to automated vehicles is one of the key paradigms in Intelligent systems. In order to improve the safety and road accidents, detection of road lanes is required to be done with more accuracy. Utilizing computer vision is a potential method to aid drivers in challenging situations, enhancing both their safety and comfort. The essential components of road lane detection include Line parameter estimation, selection of the Region of Interest (ROI) and astute edge detection. This research work aims to use computer vision techniques for the detection of road lane lines. It utilizes image processing algorithms to identify and track the road lane markings in real-time. The proposed system makes use of edge detection techniques and Hough transform to detect straight lines on the road. It then combines this information with other image processing techniques to accurately determine the lane boundaries. The algorithm is able to accurately detect and classify road lane lines in real-time, achieving a high detection accuracy of 97.5%. The performance achieved demonstrate the efficiency of the proposed method in enabling autonomous vehicles to navigate safely and stay within designated boundaries.

关键词： Computer vision Lane detection Navigation Roads image processing image edge detection Transforms

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VIBRATION-BASED BRIDGE DAMAGE DETECTION USING image-BASED PRE-TRAINED DEEP LEARNING NETWORK

VIBRATION-BASED BRIDGE DAMAGE DETECTION USING IMAGE-BASED PR...

引用

15th ASME conference on Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

作者： Song, Xi Dyogi, Joshua Cho, Chunhee Univ Hawaii Manoa Honolulu HI 96822 USA

ISBN: (纸本)9780791886274

Vibration-based damage detection has become one of the principal practices to prevent structural collapses in civil, mechanical, and other engineering disciplines. Meanwhile, with the advancement of computing technology, various machine learning (ML) approaches have been applied toward structural damage detection through the application of post-processing algorithms. To accurately predict damages with ML, large amounts of structural response data are collected from a series of sensors attached to the structure. Therefore, the damage diagnosis requires high computational efforts. To address such an issue, this paper presents a revolutionary approach utilizing an image-based pre-trained convolutional neural network (CNN) to detect bridge damage locations and severities. Our research adopted scalograms from wavelet transform to convert structure acceleration data into image data. Compared with the traditional frequency analysis derived from the Fourier transform, the new method maintains both spatial and temporal information from the original structural behaviors. To generate CNN learning features, six channels of acceleration data are gathered from six strategically selected points of a finite element (FE) bridge model. Two pre-trained CNN, AlexNet and Resnet, are selected to conduct transfer machine learning for higher training efficiency. The performances of the proposed method are assessed with various damage scenarios. The prediction accuracies of AlexNet and Resnet are 98% and 100%, respectively.

关键词： Structural Damage Detection Machine Learning Deep Learning Transfer Learning Wavelet Transform Scalogram

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Pothole Detection Using Machine Learning algorithms 15

Pothole Detection Using Machine Learning Algorithms

引用

15th International conference on Signal processing and Communication systems (ICSPCS)

作者： Al Masud, A. K. M. Jobayer Sharin, Saraban Tasnim Shawon, Khandokar Farhan Tanvir Zaman, Zakia Daffodil Int Univ Dept Comp Sci & Engn Dhaka Bangladesh Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9781665436991

Potholes are one of the greatest problems on the roads of Bangladesh. Stuck water on the roads and overloaded vehicles are mainly responsible for surface decay and erosion of rocks under the road surface which creates potholes that cause a lot of accidents and risks for general people. A system is needed that will detect potholes not only to alert the drivers but also to alert the authorities. In this paper, we emphasized on detecting the potholes using pothole image data and normal road image data. At first, we collected the data and then preprocessed them by resizing and rescaling. We used MobileNetV2 to extract the features and we reduced the dimension of the features using PCA, LDA, and t-SNE. Finally, for training, we applied five Machine Learning classification algorithms which are Support Vector Machine (SVM), Logistic Regression, Random Forest, Elastic Net, and Decision Tree. The results from Logistic Regression, Elastic Net, and Support Vector Machine (SVM) are relatively better than the other two. After setting up the three of them side by side we observed that Support Vector Machine (SVM) works best for our system and gave an accuracy of 99%.

关键词： Pothole Detection image processing Support Vector Machine

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Chicken Quality Evaluation Using Deep Learning 2nd

Chicken Quality Evaluation Using Deep Learning

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2nd International conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2023

作者： Madan, Rishi Choudhury, Tanupriya Sarkar, Tanmay Bansal, Nikunj Toe, Teoh Teik Uttarakhand Dehradun248007 India Adjunct Professor CSE Dept. Graphic Era Hill University Uttarakhand Dehradun248002 India The AI University CutbankMT59427 United States Department of Food Processing Malda Polytechnic West Bengal Malda732102 India Singapore Singapore

ISBN: (纸本)9789819919451

In recent years, the demand for food materials is reaching sky, thus making the artificial intelligence-based evaluation technique the research hot-spot now because the quality determination of these materials can be really difficult without proper understanding and experience. As a result, we have developed a classification model for artificial neural networks based on computer vision. On a primary dataset, we are utilizing a convolutional-based architecture to focus on shape and texture, which are the most crucial factors to experts. An example of a DNN (Deep Neural Network) is a convolutional neural network, which has been shown to be particularly effective in extracting features from input images. This project demonstrates that deep learning improved the accuracy of classification tasks over traditional image analysis methods. And thus, it can be inferred that it is a potential future tool for grading the food quality. We also intend to do an analysis of different machine learning algorithms available and compare it with our model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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Improving Compressive Imaging Recovery via Measurement Augmentation

Improving Compressive Imaging Recovery via Measurement Augme...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Romario Gualdrón-Hurtado Roman Jacome Leon Suarez Emmanuel Martinez Henry Arguello Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In compressive imaging systems, the scene is acquired via linear coded noisy projections, known as measurements, requiring a recovery process to estimate the underlying signal. This recovery is inherently ill-posed, posing a challenge for accurate signal recovery. Existing methods that employ prior information about the signal often fail in practical scenarios. In this work, instead of developing a new prior over the signal, we exploit the structure of the low-dimensional measurements to synthesize an augmented measurement set that can be used in various recovery methods to improve its performance. We used a deep neural network to generate the synthetic measurements from the acquired data. We show the benefits of this approach in two schemes, deep learning-based recovery and the plug-and-play (PnP) algorithm. Particularly, our method is interpreted as a non-linear preconditioning technique for the PnP algorithm. We show improved performance for different sensing matrices.

关键词： image coding Atmospheric measurements Imaging Signal processing algorithms Particle measurements Acoustic measurements Sensors Noise measurement Speech processing image reconstruction

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Representation of Observations in Reinforcement Learning for Playing Arcade Fighting Game 10th

Representation of Observations in Reinforcement Learning f...

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Proceedings of the 10th Machine Intelligence and Digital Interaction conference, MIDI 2022

作者： Du, Huaiyu Jóźwiak, Rafal Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Applied Artificial Intelligence Laboratory National Information Processing Institute Warsaw Poland

ISBN: (纸本)9783031376481

Reinforcement learning (RL) is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning algorithms have become very popular in simple computer games and games like chess and GO. However, playing classical arcade fighting games would be challenging because of the complexity of the command system (the character makes moves according to the sequence of input) and combo system. In this paper, a creation of a game environment of The King of Fighters ’97 (KOF ’97), which implements the open gym env interface, is described. Based on the characteristics of the game, an innovative approach to represent the observations from the last few steps has been proposed, which guarantees the preservation of Markov’s property. The observations are coded using the "one-hot encoding" technique to form a binary vector, while the sequence of stacked vectors from successive steps creates a binary image. This image encodes the character’s input and behavioural pattern, which are then retrieved and recognized by the CNN network. A network structure based on the Advantage Actor-Critic network was proposed. In the experimental verification, the RL agent performing basic combos and complex moves (including the so-called "desperation moves") was able to defeat characters using the highest level of AI built into the game. © 2023, The Author(s).

关键词： Reinforcement learning

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ERFM: An Efficient and Robust Detection Algorithm for Face Mask

ERFM: An Efficient and Robust Detection Algorithm for Face M...

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IEEE International conference on Signal and image processing (ICSIP)

作者： Yuanyang Hou School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In order to prevent and respond to sudden public health emergencies such as COVID-19, it has become increasingly important to accurately detect in real time whether people in public places are wearing masks. Previous deep learning-based mask detection algorithms suffer from low accuracy, poor real-time performance and robustness. To solve these problems, in this paper, we propose an efficient and robust detection algorithm (ERFM) for real-time face mask detection based on You Only Look Once version 8 (YOLOv8). Our ERFM algorithm consists of an efficient face mask detection algorithm (EFM) and a robust face mask detection algorithm (RFM). ERM, which is based on YOLOv8 object detection algorithm, is used for high-precision and real-time face mask detection task, while EFM, which is based on YOLOv8 pose estimation algorithm, is used to defend against mask imitation attacks. In comparison with other mainstream face mask detection algorithms, our ERFM shows significant advantages in robust, accuracy and speed.

关键词： COVID-19 YOLO Accuracy Computational modeling image processing Pose estimation Real-time systems

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Cascaded based Adaptive R-CNN Network for Insulator Defect Detection 23

Cascaded based Adaptive R-CNN Network for Insulator Defect D...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yao, Nan Shan, Guangrui Pan, Jianya Wang, Zhen Zhu, Xueqiong State Grid Jiangsu Electric Power Co. Ltd. Research Institute Nanjing210000 China

ISBN: (纸本)9781665494571

Target detection is a popular direction in computer vision and digital image processing. It is widely used in robot navigation, intelligent video surveillance, industrial detection, aerospace, and many other fields. It has important practical significance to reduce the consumption of human capital through computer vision. Due to the widespread use of deep learning, target detection algorithms have developed relatively rapidly. In this paper, we propose a method for detecting insulator defects Based on Cascaded Fast R-CNN. We use RoIAlign instead of RoIPooling to get a better positioning effect and add a mask branch on the basis of Faster R-CNN to complete the pixel-level segmentation task, which can detect insulator defects in real-time with high detection accuracy. © 2021 IEEE.

关键词： image segmentation Computer vision Service robots Navigation Failure analysis Object detection Insulators

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In-Sensor Learning on Magnetic Tunnel Junction Devices Using the Forward-Forward Algorithm

In-Sensor Learning on Magnetic Tunnel Junction Devices Using...

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Sensors and Information Technology (ICSI), International conference on

作者： Jiayuan Wang Minhui Ji Liyuan Yang Yueguo Hu Mengchun Pan Jiafei Hu Peisen Li College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Stomatology The Fourth Military Medical University Xian China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Aiming at the real-time processing requirements in edge computing devices, we construct in-sensor computing units based on magnetic tunnel junction (MTJ) arrays, which realizes in-situ computation of vector-matrix multiplication with parameters dynamically programmed to adapt to different tasks. Different from the previously used back-propagation (BP) algorithm, we innovatively introduce the forward-forward (FF) algorithm, which utilizes the positive and negative samples “Goodness” optimization mechanism to reduce the computational complexity. In the noisy image classification task, the FF algorithm reduces the training time by more than 90% compared to the BP algorithm and significantly improves the convergence speed when perfect classification results are achieved.

关键词： Training Heuristic algorithms Real-time systems Classification algorithms Computational efficiency Noise measurement Junctions Magnetic tunneling image classification Convergence

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Implemention of image processing Platform on Low-cost ZedBoard FPGA Using Xilinx-Vivado

Implemention of Image Processing Platform on Low-cost ZedBoa...

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Advances in Electrical and Communication Technologies (ICAECOT), International conference on

作者： Mehdi Baali Kamel Messaoudi Nadjla Bourbia El-Bay Bourennane Electrical Engineering and Renewlable Energies Laboratory (LEER) Mohemed Cherif Mssaadia University Souk-Ahras Algeria Laboratory for the Study of Electronic Materials for Medical Applications Mentouri Brothers University Constantine 1 Algeria ImVia Laboratory - Burgundy University Dijon France

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

The execution of complex functions and high computational tasks remains a major obstacle to efficient and transparent real-time processing. Overcoming these challenges is essential to unlocking the full potential of image and video processing technologies, and enabling their widespread adoption across diverse industries and applications. The main objective of this paper is to design an integrated image processing system based on ARM processor. This system aims to perform feature extraction and facilitate the high-speed of data transmission. The implementation results highlight the successful use of system resources, showing efficient allocation of hardware components and computing resources. Through careful design and optimization, we have achieved efficient use of available resources, ensuring that hardware IPs and associated algorithms are effectively exploited to achieve optimum performance.

关键词： Energy consumption Program processors Computer architecture Streaming media Hardware Real-time systems Planning Resource management Streams Optimization

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