Implementasi Deep Learning Dan Computer Vision Untuk Analisis Kerusakan Jalan
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Implementasi Deep Learning Dan Computer Vision Untuk Analisis Kerusakan Jalan
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Author : Rian Rahmanda Putra
language : id
Publisher: Penerbit NEM
Release Date : 2025-01-01
Implementasi Deep Learning Dan Computer Vision Untuk Analisis Kerusakan Jalan written by Rian Rahmanda Putra and has been published by Penerbit NEM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-01 with Business & Economics categories.
Buku ini adalah panduan komprehensif tentang penerapan deep learning dalam deteksi objek, khususnya untuk deteksi kerusakan jalan. Buku ini tidak hanya menyajikan konsep teoretis, tetapi juga dilengkapi dengan software deteksi kerusakan jalan dan kode pemrograman menggunakan algoritma Single Shot Detector (SSD) melalui platform Google Colab. Bab 1 memperkenalkan dasar-dasar deep learning dan computer vision, diikuti oleh Bab 2 yang menjelaskan elemen-elemen kunci dalam kerangka kerja computer vision. Bab 3 membahas perbedaan antara object detection dan image processing, sementara Bab 4 mengklasifikasikan berbagai pendekatan pemodelan object detection yang umum digunakan. Bab 5 mendalami arsitektur dan konfigurasi SSD, dengan pembahasan teknis mengenai parameter yang diperlukan untuk mengembangkan model deteksi objek yang efisien. Di Bab 6, pembaca diajak mempraktikkan pengembangan model SSD melalui contoh studi kasus deteksi kerusakan jalan. Buku ini ideal bagi mahasiswa, peneliti, dan praktisi yang ingin mendalami penerapan deep learning dan computer vision dalam pemantauan infrastruktur.
Pengembangan Teknologi Berbasis Ai
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Author : Ridwan Hakiki, S.E., M.M.
language : id
Publisher: Takaza Innovatix Labs
Release Date : 2024-07-31
Pengembangan Teknologi Berbasis Ai written by Ridwan Hakiki, S.E., M.M. and has been published by Takaza Innovatix Labs this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-31 with Antiques & Collectibles categories.
Kata pengantar buku ini hadir untuk memberikan pandangan mendalam mengenai perkembangan teknologi berbasis kecerdasan buatan (AI), menyoroti berbagai tantangan serta peluang yang muncul dalam proses penerapannya.
Deep Learning Dan Implementasinya Dalam Deteksi Kerusakan Jalan
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Author : La Ode Muhammad Golok Jaya
language : id
Publisher: CV. Literasi Indonesia
Release Date : 2025-01-16
Deep Learning Dan Implementasinya Dalam Deteksi Kerusakan Jalan written by La Ode Muhammad Golok Jaya and has been published by CV. Literasi Indonesia this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-16 with Art categories.
Buku ini menyajikan pemahaman mendalam tentang bagaimana teknologi pengolahan citra digital, khususnya deep learning, dapat merevolusi cara kita mengelola infrastruktur jalan. Dengan fokus pada deteksi kerusakan jalan, buku ini mengupas tuntas penggunaan citra satelit dan drone sebagai sumber data utama. Pembahasan dimulai dari dasar-dasar pengolahan citra digital, menjelaskan konsep-konsep kunci seperti segmentasi, klasifikasi, dan deteksi objek. Kemudian, buku ini masuk ke dalam dunia deep learning, dengan menyoroti arsitektur-arsitektur neural network yang paling relevan untuk deteksi kerusakan jalan, seperti You Only Look Once (YOLO). Salah satu bagian yang menarik adalah pembahasan tentang pengumpulan dan persiapan data. Buku ini memberikan materi lengkap tentang cara mengumpulkan citra satelit dan drone, serta teknik-teknik untuk menandai dan melatih model deep learning. Selain itu, buku ini juga membahas tantangan unik yang dihadapi dalam deteksi kerusakan jalan, seperti variasi kondisi cuaca, pencahayaan, dan jenis kerusakan. Sebagai penutup, buku ini menyajikan berbagai studi kasus dan contoh penerapan teknologi ini di dunia nyata. Pembaca akan diajak untuk melihat bagaimana deep learning telah digunakan untuk mendeteksi retak, lubang, dan kerusakan jalan lainnya, serta bagaimana hasil deteksi ini dapat digunakan untuk perencanaan pemeliharaan jalan yang lebih efektif.
Elements Of Deep Learning For Computer Vision
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Author : Bharat Sikka
language : en
Publisher: BPB Publications
Release Date : 2021-06-24
Elements Of Deep Learning For Computer Vision written by Bharat Sikka and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-24 with Computers categories.
Conceptualizing deep learning in computer vision applications using PyTorch and Python libraries. KEY FEATURES ● Covers a variety of computer vision projects, including face recognition and object recognition such as Yolo, Faster R-CNN. ● Includes graphical representations and illustrations of neural networks and teaches how to program them. ● Includes deep learning techniques and architectures introduced by Microsoft, Google, and the University of Oxford. DESCRIPTION Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch. This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After establishing and understanding both the primary concepts, the book addresses them together by explaining Convolutional Neural Networks(CNNs). CNNs are further elaborated using top industry standards and research to explain how they provide complicated Object Detection in images and videos, while also explaining their evaluation. Towards the end, the book explains how to develop a fully functional object detection model, including its deployment over APIs. By the end of this book, you are well-equipped with the role of deep learning in the field of computer vision along with a guided process to design deep learning solutions. WHAT YOU WILL LEARN ● Get to know the mechanism of deep learning and how neural networks operate. ● Learn to develop a highly accurate neural network model. ● Access to rich Python libraries to address computer vision challenges. ● Build deep learning models using PyTorch and learn how to deploy using the API. ● Learn to develop Object Detection and Face Recognition models along with their deployment. WHO THIS BOOK IS FOR This book is for the readers who aspire to gain a strong fundamental understanding of how to infuse deep learning into computer vision and image processing applications. Readers are expected to have intermediate Python skills. No previous knowledge of PyTorch and Computer Vision is required. TABLE OF CONTENTS 1. An Introduction to Deep Learning 2. Supervised Learning 3. Gradient Descent 4. OpenCV with Python 5. Python Imaging Library and Pillow 6. Introduction to Convolutional Neural Networks 7. GoogLeNet, VGGNet, and ResNet 8. Understanding Object Detection 9. Popular Algorithms for Object Detection 10. Faster RCNN with PyTorch and YoloV4 with Darknet 11. Comparing Algorithms and API Deployment with Flask 12. Applications in Real World
Deep Learning And Computer Vision Models And Biomedical Applications
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Author : Uma N. Dulhare
language : en
Publisher: Springer Nature
Release Date : 2025-07-18
Deep Learning And Computer Vision Models And Biomedical Applications written by Uma N. Dulhare and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-18 with Computers categories.
This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.
Fundamentals Of Deep Learning And Computer Vision
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Author : Nikhil Singh
language : en
Publisher: BPB Publications
Release Date : 2020-02-24
Fundamentals Of Deep Learning And Computer Vision written by Nikhil Singh and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-24 with Computers categories.
Master Computer Vision concepts using Deep Learning with easy-to-follow steps DESCRIPTIONÊ This book starts with setting up a Python virtual environment with the deep learning framework TensorFlow and then introduces the fundamental concepts of TensorFlow. Before moving on to Computer Vision, you will learn about neural networks and related aspects such as loss functions, gradient descent optimization, activation functions and how backpropagation works for training multi-layer perceptrons. To understand how the Convolutional Neural Network (CNN) is used for computer vision problems, you need to learn about the basic convolution operation. You will learn how CNN is different from a multi-layer perceptron along with a thorough discussion on the different building blocks of the CNN architecture such as kernel size, stride, padding, and pooling and finally learn how to build a small CNN model.Ê Next, you will learn about different popular CNN architectures such as AlexNet, VGGNet, Inception, and ResNets along with different object detection algorithms such as RCNN, SSD, and YOLO. The book concludes with a chapter on sequential models where you will learn about RNN, GRU, and LSTMs and their architectures and understand their applications in machine translation, image/video captioning and video classification. KEY FEATURESÊ Setting up the Python and TensorFlow environment Learn core Tensorflow concepts with the latest TF version 2.0 Learn Deep Learning for computer vision applicationsÊ Understand different computer vision concepts and use-cases Understand different state-of-the-art CNN architecturesÊ Build deep neural networks with transfer Learning using features from pre-trained CNN models Apply computer vision concepts with easy-to-follow code in Jupyter Notebook WHAT WILL YOU LEARNÊ This book will help the readers to understand and apply the latest Deep Learning technologies to different interesting computer vision applications without any prior domain knowledge of image processing. Thus, helping the users to acquire new skills specific to Computer Vision and Deep Learning and build solutions to real-life problems such as Image Classification and Object Detection. This book will serve as a basic guide for all the beginners to master Deep Learning and Computer Vision with lucid and intuitive explanations using basic mathematical concepts. It also explores these concepts with popular the deep learning framework TensorFlow. WHO THIS BOOK IS FOR This book is for all the Data Science enthusiasts and practitioners who intend to learn and master Computer Vision concepts and their applications using Deep Learning. This book assumes a basic Python understanding with hands-on experience. A basic senior secondary level understanding of Mathematics will help the reader to make the best out of this book.Ê Table of Contents 1. Introduction to TensorFlow 2. Introduction to Neural NetworksÊ 3. Convolutional Neural NetworkÊÊ 4. CNN Architectures 5. Sequential Models
Hands On Deep Learning For Images With Tensorflow
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Author : Will Ballard
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-31
Hands On Deep Learning For Images With Tensorflow written by Will Ballard and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-31 with Computers categories.
Explore TensorFlow's capabilities to perform efficient deep learning on images Key Features Discover image processing for machine vision Build an effective image classification system using the power of CNNs Leverage TensorFlow’s capabilities to perform efficient deep learning Book Description TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras. What you will learn Build machine learning models particularly focused on the MNIST digits Work with Docker and Keras to build an image classifier Understand natural language models to process text and images Prepare your dataset for machine learning Create classical, convolutional, and deep neural networks Create a RESTful image classification server Who this book is for Hands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.
Computer Vision Using Deep Learning
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Author : Vaibhav Verdhan
language : en
Publisher:
Release Date : 2021
Computer Vision Using Deep Learning written by Vaibhav Verdhan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. You will: Examine deep learning code and concepts to apply guiding principles to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work.
Deep Learning Applications In Computer Vision Signals And Networks
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Author : Qi Xuan
language : en
Publisher: World Scientific
Release Date : 2023-03-21
Deep Learning Applications In Computer Vision Signals And Networks written by Qi Xuan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-21 with Computers categories.
This book proposes various deep learning models featuring how deep learning algorithms have been applied and used in real-life settings. The complexity of real-world scenarios and constraints imposed by the environment, together with budgetary and resource limitations, have posed great challenges to engineers and developers alike, to come up with solutions to meet these demands. This book presents case studies undertaken by its contributors to overcome these problems. These studies can be used as references for designers when applying deep learning in solving real-world problems in the areas of vision, signals, and networks.The contents of this book are divided into three parts. In the first part, AI vision applications in plant disease diagnostics, PM2.5 concentration estimation, surface defect detection, and ship plate identification, are featured. The second part introduces deep learning applications in signal processing; such as time series classification, broad-learning based signal modulation recognition, and graph neural network (GNN) based modulation recognition. Finally, the last section of the book reports on graph embedding applications and GNN in AI for networks; such as an end-to-end graph embedding method for dispute detection, an autonomous System-GNN architecture to infer the relationship between Apache software, a Ponzi scheme detection framework to identify and detect Ponzi schemes, and a GNN application to predict molecular biological activities.
Deep Learning In Visual Computing And Signal Processing
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Author : Krishna Kant Singh
language : en
Publisher: CRC Press
Release Date : 2022-10-20
Deep Learning In Visual Computing And Signal Processing written by Krishna Kant Singh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-20 with Science categories.
An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this new volume covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more.