Download or read book Deep Learning for Crack Like Object Detection written by Kaige Zhang and published by CRC Press. This book was released on 2023-03-20 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems. This book discusses crack-like object detection problem comprehensively. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. It provides a detailed review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which are easy to understand and could be a good tutorial for introducing computer vision and machine learning.

Download or read book Deep Learning for Crack Like Object Detection written by Kaige Zhang and published by CRC Press. This book was released on 2023-03-20 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems. This book discusses crack-like object detection problem comprehensively. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. It provides a detailed review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which are easy to understand and could be a good tutorial for introducing computer vision and machine learning.

Download or read book Object Detection with Deep Learning Models written by S Poonkuntran and published by CRC Press. This book was released on 2022-11-01 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

Download or read book Deep Learning in Object Recognition Detection and Segmentation written by Xiaogang Wang and published by . This book was released on 2016 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a major breakthrough in artificial intelligence, deep learning has achieved very impressive success in solving grand challenges in many fields including speech recognition, natural language processing, computer vision, image and video processing, and multimedia. This article provides a historical overview of deep learning and focus on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos. The discussed research topics on object recognition include image classification on ImageNet, face recognition, and video classification. The detection part covers general object detection on ImageNet, pedestrian detection, face landmark detection (face alignment), and human landmark detection (pose estimation). On the segmentation side, the article discusses the most recent progress on scene labeling, semantic segmentation, face parsing, human parsing and saliency detection. Object recognition is considered as whole-image classification, while detection and segmentation are pixelwise classification tasks. Their fundamental differences will be discussed in this article. Fully convolutional neural networks and highly efficient forward and backward propagation algorithms specially designed for pixelwise classification task will be introduced. The covered application domains are also much diversified. Human and face images have regular structures, while general object and scene images have much more complex variations in geometric structures and layout. Videos include the temporal dimension. Therefore, they need to be processed with different deep models. All the selected domain applications have received tremendous attentions in the computer vision and multimedia communities. Through concrete examples of these applications, we explain the key points which make deep learning outperform conventional computer vision systems. (1) Different than traditional pattern recognition systems, which heavily rely on manually designed features, deep learning automatically learns hierarchical feature representations from massive training data and disentangles hidden factors of input data through multi-level nonlinear mappings. (2) Different than existing pattern recognition systems which sequentially design or train their key components, deep learning is able to jointly optimize all the components and crate synergy through close interactions among them. (3) While most machine learning models can be approximated with neural networks with shallow structures, for some tasks, the expressive power of deep models increases exponentially as their architectures go deep. Deep models are especially good at learning global contextual feature representation with their deep structures. (4) Benefitting from the large learning capacity of deep models, some classical computer vision challenges can be recast as high-dimensional data transform problems and can be solved from new perspectives. Finally, some open questions and future works regarding to deep learning in object recognition, detection, and segmentation will be discussed.

Download or read book Advancement of Deep Learning and its Applications in Object Detection and Recognition written by Roohie Naaz Mir and published by CRC Press. This book was released on 2023-05-10 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization. In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance. The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends. The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.

Download or read book Deep Learning in Object Detection and Recognition written by Xiaoyue Jiang and published by Springer. This book was released on 2020-11-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.

Download or read book Tutorial Object Detection People With Faster region Based Convolutional Neural Network Faster R CNN written by Eko Cahyono Putro and published by Kreatif. This book was released on 2020-09-01 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dalam istilah praktis, deep learning merupakan bagian dari machine learning. Sebuah model machine learning perlu 'diberitahu' untuk bagaimana ia menciptakan prediksi akurat, dengan terus diberikan data. Sementara model deep learning dapat mempelajari metode komputasinya sendiri, dengan 'otaknya' sendiri, apabila diibaratkan. Sebuah model deep learning dirancang untuk terus menganalisis data dengan struktur logika yang mirip dengan bagaimana manusia mengambil keputusan. Untuk dapat mencapai kemampuan itu, deep learning menggunakan struktur algoritma berlapis yang disebut artificial neural network (ANN).

Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

Download or read book Visual Object Tracking with Deep Neural Networks written by Pier Luigi Mazzeo and published by BoD – Books on Demand. This book was released on 2019-12-18 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.

Download or read book Advanced Applied Deep Learning written by Umberto Michelucci and published by Apress. This book was released on 2019-09-28 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. What You Will Learn See how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.

Download or read book Object Detection written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2024-05-04 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is Object Detection The field of computer technology known as object detection is closely associated with computer vision and image processing. Its primary objective is to identify instances of semantic objects belonging to a specific class inside digital images and videos. In the field of object detection, face detection and pedestrian detection are two areas that have received extensive attention. Object detection is useful in a wide variety of computer vision applications, including image retrieval and video surveillance, among others. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Object detection Chapter 2: Computer vision Chapter 3: Image segmentation Chapter 4: Template matching Chapter 5: Optical braille recognition Chapter 6: Deep learning Chapter 7: Convolutional neural network Chapter 8: DeepDream Chapter 9: Saliency map Chapter 10: Small object detection (II) Answering the public top questions about object detection. (III) Real world examples for the usage of object detection in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Object Detection.

Download or read book 2020 3rd International Conference on Intelligent Sustainable Systems ICISS written by IEEE Staff and published by . This book was released on 2020-12-03 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In today s world, Sustainable development is becoming a crucial part to meet the increasing demand of future generations The 3rd International Conference on Intelligent Sustainable Systems ICISS 2020 is one of the initiative toward attaining sustainable development and facilitating collaborative forums in international level This conference provides unique opportunity to bring together academicians, researchers, scientists and research scholars to share and exchange ideas and practical solutions towards achievement of intelligent sustainable systems for a more sustainable future This conference also aims to create an interdisciplinary platform to share their research ideas on developing new models and algorithms for sustainable development and provide intelligent paradigm shifts to deal with uncertainties and imprecise problems in real world

Download or read book New Industry 4 0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes written by Luis Norberto López de Lacalle and published by MDPI. This book was released on 2020-03-18 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern factories are experiencing rapid digital transformation supported by emerging technologies, such as the Industrial Internet of things (IIOT), industrial big data and cloud technologies, deep learning and deep analytics, AI, intelligent robotics, cyber-physical systems and digital twins, complemented by visual computing (including new forms of artificial vision with machine learning, novel HMI, simulation, and visualization). This is evident in the global trend of Industry 4.0. The impact of these technologies is clear in the context of high-performance manufacturing. Important improvements can be achieved in productivity, systems reliability, quality verification, etc. Manufacturing processes, based on advanced mechanical principles, are enhanced by big data analytics on industrial sensor data. In current machine tools and systems, complex sensors gather useful data, which is captured, stored, and processed with edge, fog, or cloud computing. These processes improve with digital monitoring, visual data analytics, AI, and computer vision to achieve a more productive and reliable smart factory. New value chains are also emerging from these technological changes. This book addresses these topics, including contributions deployed in production, as well as general aspects of Industry 4.0.

Download or read book Deep Learning for Object Detection in Robotic Grasping Contexts written by Jean-Philippe Mercier and published by . This book was released on 2021 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade, deep convolutional neural networks became a standard for computer vision applications. As opposed to classical methods which are based on rules and hand-designed features, neural networks are optimized and learned directly from a set of labeled training data specific for a given task. In practice, both obtaining sufficient labeled training data and interpreting network outputs can be problematic. Additionnally, a neural network has to be retrained for new tasks or new sets of objects. Overall, while they perform really well, deployment of deep neural network approaches can be challenging. In this thesis, we propose strategies aiming at solving or getting around these limitations for object detection. First, we propose a cascade approach in which a neural network is used as a prefilter to a template matching approach, allowing an increased performance while keeping the interpretability of the matching method. Secondly, we propose another cascade approach in which a weakly-supervised network generates object-specific heatmaps that can be used to infer their position in an image. This approach simplifies the training process and decreases the number of required training images to get state-of-the-art performances. Finally, we propose a neural network architecture and a training procedure allowing detection of objects that were not seen during training, thus removing the need to retrain networks for new objects.

Download or read book Neural Networks Tricks of the Trade written by Grégoire Montavon and published by Springer. This book was released on 2012-11-14 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.

Download or read book 2016 14th International Conference on Control Automation Robotics and Vision ICARCV written by IEEE Staff and published by . This book was released on 2016-11-13 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The scope includes but not limited to control, automation, robotics, and vision

Download or read book Artificial Intelligence and Technologies written by Rajeev R. Raje and published by Springer Nature. This book was released on 2021-12-16 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes refereed proceedings of the 3rd International Conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies. This book covers a wide range of topics—vision, analytics, robotics, networking, health care, current pandemic issues of COVID-19, and cutting-edge technologies connected to cybersecurity in digital manufacturing and Industry 4.0. The contents of this book will be useful to researchers from industry and academia. The volume includes novel contributions and the latest developments from researchers across industry and academia. The book will serve as a valuable reference resource for academics and researchers across the globe.