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Classification of Potholes using Convolutional Neural Network Model: A Transfer Learning Approach using Inception ResnetV2 2

Classification of Potholes using Convolutional Neural Networ...

引用

2nd Edition of IEEE Delhi Section Owned conference, DELCON 2023

作者： Singh, Saravjeet Chhabra, Rishu Moudgil, Aditi Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350322064

Rough and damaged roads give poor ride quality which leads to poor transport experience, high travel cost, physical loss to vehicles and passengers, and high vehicle maintenance cost. Therefore, to plan a safe and optimal road trip, prior information of road conditions is most important. Poor road conditions are also responsible for traffic accidents. There exist many road conditions and potholes detected techniques and these techniques can be categorized into two basic categories named as vision-based techniques and vibration-based techniques. In this paper vision-based technique is used for road image classification. For the classification, a transfer learning-based Inception ResnetV2 transfer learning-based Convolutional Neural Network model is used. The selected model is trained and tested on 3211 road images. These images were collected from the public data available on the internet and captured using the camera. data is classified into three categories plane road, large pothole, and small pothole. The accuracy of the classification is calculated in terms of precision, recall, F1-score, support, and accuracy percentage. According to performed analysis, the Inception ResnetV2 transfer learning-based Convolutional Neural Network model attained maximum accuracy of 94.42 percent with a 0.933 precision value. The performance of the model during the classification process is evaluated using the training and testing loss. Further, the accuracy of the proposed model is compared with Convolution Neural Network and Support Vector Machine using the same dataset. This paper also provides a comparative analysis of the proposed model with other published work. © 2023 IEEE.

关键词： Neural network models

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Transformer terminal feature recognition and positioning method based on binocular vision and image processing algorithm 2

Transformer terminal feature recognition and positioning met...

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2nd International conference on Optics and Communication Technology, ICOCT 2022

作者： Chen, Lingying Zhao, Yue Li, Xing Electric Power Research Institute Shandong Electric Power Company of State Grid Shandong Jinan China

ISBN: (纸本)9781510660533

Based on binocular vision and image processing algorithm, the recognition and location of high voltage side and low voltage side of transformer were completed for transformer automatic wiring positioning platform. On the basis of binocular vision calibration and 3D point cloud reconstruction, P3P algorithm was used to complete the positioning of transformer operation space coordinate system. Based on the image threshold segmentation algorithm, the position of transformer high voltage terminal, low voltage terminal, and transformer rotation angle were identified on the basis of 3d point cloud data. The target position information was provided for transformer automatic wiring device. © 2022 SPIE.

关键词： Calibration

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image Segmentation for Self-Driving Car 2

Image Segmentation for Self-Driving Car

引用

2nd IEEE International conference on Intelligent Technologies, CONIT 2022

作者： Gautam, Sanchit Mathuria, Tarosh Meena, Shweta Delhi Technological University Department of Software Engineering Delhi India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

Machine learning plays a vital role in making a car autonomous. The machine learning algorithms interpret the surrounding data collected from the car's sensors and camera and perform desired actions. Semantic segmentation is one of the various techniques which can be used in self-driving cars. High computational costs will be needed to obtain higher accuracy in semantic segmentation, which is not suited for embedded systems. So, there is a need to get high accuracy models with low computational requirements. This paper used a U-Net model for the semantic segmentation task, which shows good accuracy. We will be using the cityscape image pair dataset to train and test our model. We will evaluate our model based on the Receiver Operating Characteristic (ROC) curve and Intersection over Union (IOU). © 2022 IEEE.

关键词： Autonomous vehicles

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Frequency Response Function Identification from incomplete data: A Wavelet-based Approach 2nd

Frequency Response Function Identification from Incomplete D...

引用

2nd Modeling, Estimation and Control conference (MECC)

作者： Dirkx, Nic Tiels, Koen Oomen, Tom ASML Res Mechatron Control Veldhoven Netherlands Eindhoven Univ Technol Dept Mech Engn Control Syst Technol Eindhoven Netherlands Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands

Frequency Response Function (FRF) identification plays a crucial role in the design, the control, and the analysis of complex dynamical systems, including thermal and motion systems. Especially for applications that require long measurements, missing data samples, e.g., due to interruptions in the data transmission or sensor failure, often occur. The aim of this paper is to accurately identify nonparametric FRF models of periodically excited systems from noisy output measurements with missing samples. The presented method employs a wavelet-based transformation to address the identification problem in the time-frequency plane. A simulation example confirms that the developed techniques produce accurate estimates, even when many samples are missing. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0)

关键词： Frequency domain identification non-parametric methods missing data transient estimation linear systems

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Single image Super-Resolution reconstruction Network with Dual Attention Mechanism 2

Single Image Super-Resolution Reconstruction Network with Du...

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2nd IEEE International conference on Big data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021

作者： Huang, Youwen Tang, Xin Zhou, Bin Jiangxi University of Science and Technology College of Information Engineering Ganzhou China

ISBN: (纸本)9780738131221

Aiming at the problem that the solution space of mapping function from low resolution image to high resolution image is extremely large, which makes it difficult for super-resolution reconstruction models to generate detailed textures, this paper proposes a single image super resolution reconstruction network combining dual attention mechanism. The improved U-Net network model is used as the basic structure, and the data enhancement method is introduced to increase the diversity of samples. The encoder is composed of a convolution layer and an adaptive parameter linear rectifier function (Dynamic ReLU). The image size is decreased step by step through the subsampling operation. The decoder part was composed of Residual Dual Attention Module (RDAM) and Pixelshuffle Module. The image was enlarged gradually through the up-sampling operation, and dual regression loss was introduced to enhance the network constraints. The experimental results show that the proposed method can make the reconstructed image texture more detailed and reduce the possible solution space of the mapping function effectively. © 2021 IEEE.

关键词： image texture

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Recognition and Classification of Medical images using Convolutional Neural Network 2

Recognition and Classification of Medical Images using Convo...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Makandar, Aziz Jadhav, Nayan Karnataka State Akkamahadevi Women's University Department of Computer Science Karnataka Vijayapur India

ISBN: (纸本)9798350369960

Pneumonia diagnosis is indeed challenging and error-prone to diagnose in chest X-ray pictures as other lung infection transparency is also prominent in the scan. Manually analysing X-rays by different professionals can yield distinct conclusions and inaccuracies. Implementing convolutional neural network (CNN) based models that can correctly categorize specific pneumonia categories can help with targeted treatment. The objective of this study is to utilize a pre-trained CNN variation along with Mobile Net in order to train a data set from an open source comprised of clinical images of an X-ray, followed by a training and validation analysis of the method. By analysing the data, the accuracy and adaptability of the CNN model has been evaluated. The highlights of the study defines that the imbalanced data has been balanced and enhanced the true positive prediction with improved accuracy and precision. © 2023 IEEE.

关键词： Chest X-ray Convolutional Neural Network medical image classification Pneumonia recognition

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Airport video aircraft tracking and data fusion method at remote tower 9

Airport video aircraft tracking and data fusion method at re...

引用

9th International conference on Computer and Communications, ICCC 2023

作者： Wang, Kai Gao, Yong Tang, Mozhen Zhu, Pan Chen, Tong Xue, Kang Civil Aviation Administration of China The 2nd Research Institute Chengdu China

ISBN: (纸本)9798350317251

To solve the problem that airport video data is single, low monitoring accuracy and flight information discrete at remote tower, airport video aircraft tracking and data fusion method at remote tower is proposed. Firstly, the global positioning coordinates and its corresponding images are collected in the camera's field of view. Then the relationship matrix between the GPS (Global Positioning System) coordinate system and image coordinate system is obtained by image homography model. Moreover, the pixel coordinates of the aircraft ADS-B (Automatic Dependent Surveillance - Broadcast) data is calculated by the combining the homography matrix and the aircraft GPS coordinates extracted from the civil aviation data;the aircraft positioning in video is obtained by detection and tracking algorithm. Finally, the relevant information is displayed in video by matching the aircraft trajectory and the ADS-B data trajectory. Experiments in typical airport show the proposed method can not only accurately detect the aircraft position, but also accurately display the corresponding flight information in video. © 2023 IEEE.

关键词： Towers

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CNN based Static Hand Gesture Recognition using RGB-D data 2

CNN based Static Hand Gesture Recognition using RGB-D Data

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2nd International conference on Artificial Intelligence and Signal Processing, AISP 2022

作者： Kumar, Dayananda N.C. Suresh, K.V. Dinesh, R. Dept. of Electronics and Communication Siddaganga Institute of Technology Tumkur India Dept. of Information Science and Engineering Jain University Bangalore India

ISBN: (纸本)9781665442909

Hand gesture recognition refers to identification of various hand postures which interprets the signs of non verbal communication. It finds various applications like Sign Language Recognition (SLR), Human Computer Interaction (HCI) for robotics control, 3D modeling etc., Efficiently recognizing the hand gestures in various complex background scenarios is still a challenging problem. This issue can be effectively addressed by using depth data as a additional cue along with RGB image. Depth refers to the distance between camera sensor and image scene, hence depth cues can be used in suppressing the complex backgrounds which are far away from the hand region. Depth can also be effectively used to handle the illumination issues. In this paper, we propose a two stage approach where first stage involves k-means algorithm based depth clustering and removal of the background region. In the later stage, the foreground filtered depth map is fused with RGB and the resultant RGB-D data is used for gesture recognition using Convolutional Neural Network (CNN) classification model. Experiments are conducted on OUHAndS datasets and the results are compared with well known existing methods. Experimental result shows that accuracy of 87.57% can be achieved on OUHAndS test dataset using the proposed method. © 2022 IEEE.

关键词： 3D modeling

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Extraction of urban built-up areas in Shanghai based on night-time lighting data 2

Extraction of urban built-up areas in Shanghai based on nigh...

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2023 2nd International conference on Remote Sensing, Surveying, and Mapping, RSSM 2023

作者： Yu, Wenlong Gao, Fanqiang Zhang, Mingyu Li, Bin Cao, Xiangyang School of Civil Engineering Shandong Jiaotong University 5001 Haitang Road Changqing District Shandong Province Jinan City250357 China Secondary Dam Water Control Project Management Bureau Weishan County Shandong Province Jining City277600 China

ISBN: (纸本)9781510666436

Compared to traditional multi-spectral information, nighttime light data has more intuitive features and is more sensitive to feedback on human-acquired trends such as urban sprawl and industrial development. Therefore, this paper uses Shanghai as the study area and combines the 30m resolution land use raster data with the monthly NPP-VIIRS data to determine the threshold value using the spatial comparison method of higher resolution image data. The threshold dichotomy was used to extract the urban built-up areas from the nighttime light images, and the error was only 0.85 when compared with the statistical data. © 2023 SPIE · 0277-786X.

关键词： Lighting

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Masked image Self-Learning and Knowledge Distillation for Source-Free Unsupervised Domain Adaptation in Crowd Counting 2

Masked Image Self-Learning and Knowledge Distillation for So...

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2nd International conference on image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Zeng, Jia Ma, Chaoqun Shao, Penghui Qing, Anyong Wang, Yang Southwest Jiaotong University School of Electrical Engineering Chengdu China Concordia University Department of Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9798350331417

Unsupervised Domain Adaptation (UDA) techniques leverage labeled data from a source domain to adapt to unlabeled data from a target domain, offering a primary solution for addressing cross-domain challenges in crowd counting. However, in real-world scenarios, accessing source data during the model adaptation process within the target domain can be impractical due to privacy and security concerns. This gives rise to a practical yet challenging issue known as Source-Free Unsupervised Domain Adaptation (SFUDA), where model adaptation is limited to the utilization of an existing source model and unlabeled target data. To tackle this challenge, we propose an algorithm that combines mask image self-learning and knowledge distillation to address SFUDA in crowd counting effectively. Specifically, the student model uses masked images to self-learn at both feature and output levels, exploring clues and contextual relationships around masked image blocks to predict occluded areas. Furthermore, we employ knowledge distillation to boost the model's performance within the teacher-student framework, encompassing feature extraction and density map generation stages. To mitigate cumulative errors, we stochastically recovery a subset of parameters from the source model. We conducted extensive experiments to compare our approach with a modified SFUDA algorithm, and the results demonstrate competitive performance across multiple datasets. © 2023 IEEE.

关键词： Crowd counting Domain adaptation Knowledge distillation Self-learning Source-free

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