Deep Learning for Crack-like Object Detection

Main
Computers - Artificial Intelligence (AI)
Deep Learning for Crack-like Object...

Deep Learning for Crack-like Object Detection

Kaige Zhang, Heng-Da Cheng

5.0 / 5.0

Bu kitabı ne kadar beğendiniz?

İndirilen dosyanın kalitesi nedir?

Kalitesini değerlendirmek için kitabı indirin

İndirilen dosyaların kalitesi nedir?

Accurately detecting crack localization is not an easy task. This book addresses important issues in detecting crack-like objects and provides a practical smart pavement surface inspection system using deep learning.

Kategoriler:

Computers - Artificial Intelligence (AI)

Yıl:

2023

Yayımcı:

CRC Press

Dil:

english

Sayfalar:

107

ISBN 10:

1032181184

ISBN 13:

9781000871272

Dosya:

PDF, 37.62 MB

IPFS:

,

english, 2023

Online Oku

'e dönüştürme devam ediyor

dosyasına dönüştürme başarısız oldu

İlginizi çekebilir

郭贵春 — 自然辩证法

Marco Scutari, Mauro Malvestio, (eds.) — The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions

Arash Gharehbaghi — Deep Learning in Time Series Analysis

Roshani Raut, Pranav D Pathak, Sachin R Sakhare, Sonali Patil — Generative Adversarial Networks and Deep Learning

L. Ashok Kumar, D. Karthika Renuka — Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision: Techniques and Use Cases

Fei Hu — AI, Machine Learning and Deep Learning: A Security Perspective

Mukesh D Patil, Gajanan K Birajdar, Sangita S Chaudhari, (ed.) — Computational Intelligence in Image and Video Processing

Jyotismita Chaki, Ayşegül Uçar — Current Applications of Deep Learning in Cancer Diagnostics

Bhowmik, Mrinal Kanti, — Computer Vision: Object Detection In Adversarial Vision

Fei Hu, Iftikhar Rasheed — Deep Learning and Its Applications for Vehicle Networks

S. Poonkuntran, Rajesh Kumar Dhanraj, Balamurugan Balusamy — Object Detection with Deep Learning Models: Principles and Applications

Kaige Zhang, Heng-Da Cheng — Deep Learning for Crack-Like Object Detection

Tariq M. Arif, Adilur Rahim — Deep Learning for Engineers

Uzair Aslam Bhatti, Huang Mengxing, Jingbing Li, Sibghat Ullah Bazai, Muhammad Aamir, (eds.) — Deep Learning for Multimedia Processing Applications: Volume Two: Signal Processing and Pattern Recognition

David Foster — Generative Deep Learning, 2nd Edition (6th Early Release)

Sofien Kaabar — Deep Learning for Finance

Philipp Grohs (editor), Gitta Kutyniok (editor) — Mathematical Aspects of Deep Learning

Basha, Niha Kamal;Bhatia Khan, Surbhi;Kumar, Abhishek;Mashat, Arwa;; Surbhi Bhatia Khan; Abhishek KumarArwa Mashat — Deep Learning and Its Applications Using Python

Dustin A. Coates — Voice Applications For Alexa And Google Assistant

Dev, Dipayan — Deep learning with Hadoop : build, implement and scale distributed deep learning models for large-scale datasets

Maokun Li, Marco Salucci, (ed.) — Applications of Deep Learning in Electromagnetics: Teaching Maxwell's equations to machines

Amita Nandal, Liang Zhou, Arvind Dhaka, Todor Ganchev, Farid Nait-Abdesselam, (eds.) — Machine Learning in Medical Imaging and Computer Vision

[英国] 安东尼·斯托尔 — 丘吉尔的黑狗

[美] 伦纳德·泰尔米（Leonard Talmy） — 认知语义学卷Ⅱ 概念构建的类型和过程

国家法官学院案例开发研究中心 — 中国法院2017年度案例：劳动纠纷（含社会保险纠纷）

Roohie Naaz Mir, Vipul Kumar Sharma, Ranjeet Kumar Rout, Saiyed Umer — Advancement of Deep Learning and its Applications in Object Detection and Recognition

Shekhar Khandelwal — Deep Learning for Data Architects

Gyanendra K. Verma (editor), Badal Soni (editor), Salah Bourennane (editor), Alexandre C. B. Ramos (editor) — Data Science: Theory, Algorithms, and Applications (Transactions on Computer Systems and Networks)

Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann, (eds.) — Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide

M. N. Murty, M. Avinash — Representation in Machine Learning

Mohd Hafiz Arzmi, Anwar P.P. Abdul Majeed, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Hong-Seng Gan, Ismail Mohd Khairuddin, Ahmad Fakhri Ab. Nasir — Deep Learning in Cancer Diagnostics: A Feature-based Transfer Learning Evaluation

A. losifidis, A. Tefas — Deep Learning for Robot Perception and Cognition

Shih-Chia Huang, Trung-Hieu Le — Principles and Labs for Deep Learning

Roshani Raut, Pranav D Pathak, Sachin R Sakhare, Sonali Patil — Generative Adversarial Networks and Deep Learning

Mehdi Ghayoumi (University of San Diego) — Generative Adversarial Networks in Practice

Mahmoud Hassaballah; Ali Ismail Awad — Deep Learning in Computer Vision: Principles and Applications

Jakub Langr, Vladimir Bok — GANs in Action

Ganguly, Kuntal — Learning generative adversarial networks : next-generation deep learning simplified

Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis — Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III

Qian Wang, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Badri Roysam, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le — Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China,

Tom Gedeon, Kok Wai Wong, Minho Lee — Neural Information Processing: 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part I

Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm — Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII

Anahtar ifadeler

İlgili Koleksiyonlar