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Deep Learning Approach for Automated Railroad Ballast Condition Evaluation 5th

Deep Learning Approach for Automated Railroad Ballast Condit...

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5th international conference on Transportation Geotechnics

作者： Luo, Jiayi Ding, Kelin Qamhia, Issam I. A. Hart, John M. Tutumluer, Erol Univ Illinois Dept Civil & Environm Engn 205 N Mathews Urbana IL 61801 USA Univ Illinois Beckman Inst Adv Sci & Technol 405 N Mathews Urbana IL 61801 USA

ISBN: (纸本)9789819782154;9789819782130;9789819782123

Ballast has a significant impact on track performance, and the evaluation of ballast condition is crucial for safe railroad operations. this paper focuses on a Ballast Scanning Vehicle (BSV) recently developed for automating ballast inspection using a deep learning-based, computer vision approach. Traditional evaluation methods, e.g., visual inspection or ballast sampling followed by sieve analysis, are subjective and labor-intensive. Furthermore, ballast samples collected from a single location/depth may not be representative of accurately revealing variations of degradation and ballast condition along the track. In contrast, the BSV employs three image acquisition devices to continuously capture high-quality scans of ballast cut sections, enabling accurate and in-depth evaluation of continuous sections of the track. the deep-learning framework was trained to process acquired ballast scans, generating image-based metrics including percent degraded segments (PDS), fouling index (FI) estimates, and in-service ballast gradations. the accompanying user-friendly graphical interface integrates all data processing algorithms and provides comprehensive visualizations of results. Field data was collected using the BSV, from cut trenches opened using a ballast regulator, along the High Tonnage Loop (HTL) at the Transportation Technology Center (TTC) in the U.S. the FI and gradations from the BSV were compared to laboratory sieve analyses and Ground Penetrating Radar (GPR) data. Additional laboratory tests with various fouling conditions were conducted to validate the deep learning algorithms and clarify any differences between sieving results and algorithm estimates possibly attributed to sampling issues. this field deployment demonstrated that the BSV could accurately evaluate ballast conditions close to real time, thus making it a robust system for quantifying ballast degradation.

关键词： Railroad Ballast Fouling Inspection Track vehicle image segmentation Deep learning

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5th international conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

5th International Conference on Medical Imaging and Computer...

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5th international conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

ISBN: (纸本)9789819638628

the proceedings contain 53 papers. the special focus in this conference is on Medical Imaging and Computer Aided Diagnosis. the topics include: Deep Learning Frameworks for Histopathological image Processing in Colorectal Cancer Diagnostics;Improving Knee Osteoarthritis Detection through a Multitask Learning Method from 2D MRI Slices;enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study;retNet: A Two-Stage Deep Neural Network for Clinically Traceable Retinal Disease Classification;Attention-Based CNN for Enhanced Detection of Arsenic Exposure;deep Learning for Lymph Node Metastasis Detection in Pancreatic Ductal Adenocarcinoma;Knowledge Distillation for Enabling Efficient AI-Based Skin Cancer Detection in Resource-Constrained Environments;classification Method in Vision Transformer with Explainability in Medical images for Lung Neoplasm Detection;BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical image Classification;increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches;Synth-to-Segment: MRI Brain Tumor Segmentation with Diffusion Transformers and Attention U-Net;(ExMod) Model for Medical image Segmentation Using Scribble Annotations;LV-Mamba: Integrating Denoising Mechanism with Mamba for Improved Segmentation of the Pediatric Echocardiographic Left Ventricle;LoRA-MedSAM: Efficient Medical image Segmentation;dependency-Related Skin Lesion Bed and Periwound Segmentation Trained on Partially Annotated Clinical images;rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion;Explainable Deep Learning Framework for Ground Glass Opacity (GGO) Segmentation from Chest CT Scans;Multi-statistical Features-Based 4D Spatiotemporal Level Set Model for CTP image Segmentation;deep Learning Based Segmentation of Magnetic Resonance Cardiac images;generic Liv

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A Comparative Study of Deep Learning Algorithms for Glaucoma Classification Using Retinal images 5th

A Comparative Study of Deep Learning Algorithms for Glaucoma...

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5th international conference on Data Science, Machine Learning and Applications

作者： Swapna, T. Varshitha, Y. Sai Raja Sudeepthi, K. L. Manavika, B. Saishree, T. G Narayanamma Inst Technol & Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

A group of eye conditions known as glaucoma impair the optic nerve, which is in charge of sending visual data from the eye to the brain. Glaucoma impacts 3.54% of adults aged 40 to 80 around the world. Early detection of glaucoma is crucial as it can prevent total optic nerve damage, which would cause irreversible vision loss. It is possible for specialists to diagnose glaucoma medically, but treatment options are either expensive or time-consuming and requires ongoing care from medical professionals. there have been numerous initiatives at streamlining all components of the glaucoma categorization process, however these models are challenging for users to comprehend the key predictors, resulting in them being unreliable for use by medical experts. the study uses eye fundus images to classify glaucoma patients using three distinct Deep Learning techniques: Convolutional neural network, Visual Geometry Group 16 (VGG16), and Global Context Network (GC-Net). In addition, several data pre-processing techniques are used to avoid overfitting and achieve high accuracy. this research compares and analyses the performance of various architectures using the aforementioned techniques. the CNN model had the best accuracy of 83% when in contrast to the other deep learning models.

关键词： Biomedical image processing Glaucoma Blind ness Machine learning Visual geometry group 16 Convolutional neural network Global context network

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Multi-scale pansharpening method based on frequency feature guidance 5

Multi-scale pansharpening method based on frequency feature ...

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5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Song, Tianxin Jian, Lihua Zhang, Di School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Focused on the issues of blurring effect and spectral distortion in current pansharpening approaches, we propose a multiscale pansharpening method based on frequency feature guidance. Firstly, we extract frequency features using learnable Discrete Wavelet Transform Layers (DWTL), select key frequency features for fusion, and then use Inverse Discrete Wavelet Transform Layers (IDWTL) to transform the fused features back to the spatial domain to guide image reconstruction. Secondly, we employ a multi-scale progressive strategy to reconstruct the fused image, effectively leveraging the spectral and spatial features of the source images at different scales. Additionally, we implement a multiscale reconstruction loss constraint during network training to further enhance fusion accuracy. the superiority of our method is shown by testing results on two datasets, both at reduced and full resolution. © 2025 SPIE.

关键词： image reconstruction

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Application of conditional DDPM on the MNIST dataset 5

Application of conditional DDPM on the MNIST dataset

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5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Wang, Xin Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Since its introduction, Denoising Diffusion Probabilistic Models (DDPM) have received widespread attention for their exceptional performance in image generation. they generate new samples by simulating the denoising process of data, a method that is not only simple and efficient but also capable of producing highly realistic samples. this paper explores the application of Conditional Denoising Diffusion Probabilistic Models (Conditional DDPM) on the MNIST dataset. MNIST is a classic dataset containing handwritten digit images, widely used in computer vision and machine learning fields. the paper first introduces the basic principles and model structure of Conditional DDPM, then elaborately explains how to train and apply the Conditional DDPM on the MNIST dataset, and analyzes the experimental results. the experimental results show that the Conditional DDPM can generate high-quality handwritten digit images that meet specific conditions on the MNIST dataset. © 2025 SPIE.

关键词： image denoising

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Study on the application of transfer learning in small sample image classification of military equipment 5

Study on the application of transfer learning in small sampl...

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5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Lu, Leiji Yu, Hongxia Xiao, Hongju Bao, Lei Department of Information Engineering Army Academy of Artillery and Air Defense Hefei China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

To address the issue of insufficient military equipment sample data, which cannot meet the training requirements of deep neural networks and tends to cause overfitting, this paper introduces transfer learning technology to solve the small-sample classification problem for military equipment. By constructing a multi-type sample training set and fine-tuning the convolutional layers of pre-trained models, specific target classifiers are trained. Practice has proven that the application of transfer learning in small-sample classification tasks saves model training time, resolves issues related to model overfitting and strong dependence on data labels, and effectively improves the accuracy of image classification based on deep learning for small samples of military equipment. © 2025 SPIE.

关键词： Deep neural networks

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Application of deep transfer learning on UAV-based aerial images for forest fire detection 5

Application of deep transfer learning on UAV-based aerial im...

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5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Yu, Feng International Education College Beijing University of Chemical Technology Beijing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

the prompt detection and timely response to forest fires are crucial for effectively protecting the ecological environment. Despite the progress of deep learning fire image recognition models, in UAV aerial image processing, traditional models face challenges such as small fire points, smoke interference, and complex backgrounds, resulting in unsatisfactory detection accuracy. To cope with such problems, this paper proposes a forest fire detection method for UAV aerial photography based on a pre-trained DenseNet201 model, which is trained by using the FLAME dataset and combined with a channel and spatial attention mechanism module (CBAM). this model fire detection accuracy reaches 88.63% and the F-score can reach 90.02%. the model's performance was further validated by visual analysis of the confusion matrix, classification report, ROC curve, and precision-recall curve. the method therefore has a broad application prospect in real forest fire monitoring. © 2025 SPIE.

关键词： Aerial photography

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

5th International Conference on Data Science, Machine Learni...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

ISBN: (纸本)9789819780426

the proceedings contain 128 papers. the special focus in this conference is on Data Science, Machine Learning and Applications. the topics include: Digitization of Monuments – An Impact on the Tourist Experience with Special Reference to Hampi;resume Parser Using Machine Learning;IOT Based Smart Hydroponics System;comparative Study of Machine Learning and Deep Learning Techniques for Cancer Disease Detection;High thruput Modulation Approaches Used in Next Generation WiF’s Under Multi-impairments Environments with MATLAB Codes;skin Disease Detection;root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors;deep Learning Model Development for an Automatic Healthcare Edge Computing Application;Empathetic Conversations in Mental Health: Fine-Tuning LLMs for Supportive AI Interactions;exploring Block Chain Technology with Applications, and Future Prospects;a Comprehensive Review of Soft Computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead;Performance Analysis of Machine Learning Algorithms on Imbalanced Datasets Using SMOTE Technique;An AI Based Nutrient Tracking and Analysis System;power Saving Mechanism for Street Lights System Using IoT;Automatic Login System Using ATTINY85 IC;forecasting Stock Prices: A Comparative Analysis of Machine Learning, Deep Learning, and Statistical Approaches;smart Vision Bot;robots in Logistics: Apprehension of Current Status and Future Trends in Indian Warehouses;smart Healthcare: Enhancing Patient Well-Being with IoT;Detection of B-ALL Using CNN Model and Deep Learning;a Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image Processing;a Comprehensive Survey on Enhancing Patient Care through Deep Learning and IoT-Enabled Healthcare Innovations;attention-Based image Caption Generation.

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

5th International Conference on Data Science, Machine Learni...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

ISBN: (纸本)9789819780303

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Spatial vs. Spectral Resolution, Which Is Important for Lychee and Longan Classification from Hyperspectral Remote Sensing image 16

Spatial vs. Spectral Resolution, Which Is Important for Lych...

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2024 16th international conference on graphics and image Processing, ICGIP 2024

作者： Peng, Changkui Luo, Wenfei Yuan, Tingting Wu, Wenyan Zhu, Yufang Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China Ministry of Natural Resources Guangzhou510663 China School of Geography South China Normal University Guangzhou510631 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Lychee and longan are important economic crops in South China. Fast identifying and acquiring their planting areas are important tasks. However, lychee trees and longan trees have similar appearances. So, they have similar spectral features. therefore, obtaining the planting area of these trees from a remote sensing image becomes a challenge. Furthermore, unlike lychee, the planting area of longan in Guangdong province is too small to be identified. therefore, spatial and spectral resolution may become important factors for the classification. To evaluate the influence of spatial and spectral resolution on lychee and longan classification, this paper compares different kinds of representative remote sensing data sources, including Sentinel-2(high spatial resolution, multispectral image), Zhuhai-1(high spatial resolution, hyperspectral image), Gaofen-5(low spatial resolution, hyperspectral image), Ziyuan-1 02D (low spatial resolution, hyperspectral image), as well as the fused image of GF-5 with Sentinel-2. We aim at finding which spatial and spectral resolution can be suitable for classification. We combine a hierarchical classifier and convolutional neural networks (CNNs) to classify the classes. the hierarchical classifier classifies the land use classes and the combined class of lychee and longan at the first level. And then it classifies lychee and longan from the combined class at the second level. the CNN is used as a base classifier in the hierarchical classifier. three branches, i.e. spatial feature, spectral feature, and the joint of spatial and spectral feature branches, are the backbone of the network. 1D, 2D and 3D convolutional layers are designed to extract the spectral features, the spatial features and the joint features in the above branches, respectively. the experimental results show that spectral resolution is the most important factor for the lychee and longan classification, while spatial resolution cannot be ignored since longan occupies small

关键词： Spectral resolution

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