This book explores intelligent object detection technology in industrial workshop scenarios in depth, evaluating its potential to transform traditional manual operations into digital, networked, and intelligent paradigms by serving as the "perceptual core" of smart workshops. It provides fundamental technical support for critical applications such as production line inspection, safety monitoring, mobile robot navigation, and human-machine collaboration—scenarios inherently exposed to complex environmental backgrounds, diverse object scales, and high real-time requirements. Research on industrial workshop object detection has attracted professionals and scholars from multiple disciplines, including computer vision, deep learning, industrial automation, and intelligent manufacturing. Adopting a systematic approach, the book constructs a comprehensive technical framework covering dataset construction, 2D semantic segmentation, depth camera calibration, depth image inpainting, 3D object detection, and 3D instance segmentation, while emphasizing the significance of multi-modality fusion and cross-dimensional learning in addressing core technical challenges. The book is intended for undergraduate and graduate students interested in intelligent object detection, researchers exploring computer vision and industrial intelligence, and engineers engaged in the development of smart factory systems, autonomous robots, or industrial automation applications.

This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions.Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions.The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning.

This book investigates detailed hyperspectral image clustering using graph neural network (graph learning) methods, focusing on the overall construction of the model, design of self-supervised methods, image pre-processing, and feature extraction of graph information. Multiple graph neural network-based clustering methods for hyperspectral images are proposed, effectively improving the clustering accuracy of hyperspectral images and taking an important step towards the practical application of hyperspectral images. This book is innovative in content and emphasizes the integration of theory with practice, which can be used as a reference book for graduate students, senior undergraduate students, researchers, and engineering technicians in related majors such as electronic information engineering, computer application technology, automation, instrument science and technology, remote sensing.

This book deals with hyperspectral image classification using graph neural network methods, focusing on classification model designing, graph information dissemination, and graph construction. In the book, various graph neural network based classifiers have been proposed for hyperspectral image classification to improve the classification accuracy. This book has promoted the application of graph neural network in hyperspectral image classification, providing reference for remote sensing image processing. It will be a useful reference for researchers in remote sensing image processing and image neural network design.

This book explores intelligent object detection technology in industrial workshop scenarios in depth, evaluating its potential to transform traditional manual operations into digital, networked, and intelligent paradigms by serving as the "perceptual core" of smart workshops. It provides fundamental technical support for critical applications such as production line inspection, safety monitoring, mobile robot navigation, and human-machine collaboration—scenarios inherently exposed to complex environmental backgrounds, diverse object scales, and high real-time requirements. Research on industrial workshop object detection has attracted professionals and scholars from multiple disciplines, including computer vision, deep learning, industrial automation, and intelligent manufacturing. Adopting a systematic approach, the book constructs a comprehensive technical framework covering dataset construction, 2D semantic segmentation, depth camera calibration, depth image inpainting, 3D object detection, and 3D instance segmentation, while emphasizing the significance of multi-modality fusion and cross-dimensional learning in addressing core technical challenges. The book is intended for undergraduate and graduate students interested in intelligent object detection, researchers exploring computer vision and industrial intelligence, and engineers engaged in the development of smart factory systems, autonomous robots, or industrial automation applications.

This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions.Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions.The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning.

This book investigates detailed hyperspectral image clustering using graph neural network (graph learning) methods, focusing on the overall construction of the model, design of self-supervised methods, image pre-processing, and feature extraction of graph information. Multiple graph neural network-based clustering methods for hyperspectral images are proposed, effectively improving the clustering accuracy of hyperspectral images and taking an important step towards the practical application of hyperspectral images. This book is innovative in content and emphasizes the integration of theory with practice, which can be used as a reference book for graduate students, senior undergraduate students, researchers, and engineering technicians in related majors such as electronic information engineering, computer application technology, automation, instrument science and technology, remote sensing.

This book deals with hyperspectral image classification using graph neural network methods, focusing on classification model designing, graph information dissemination, and graph construction. In the book, various graph neural network based classifiers have been proposed for hyperspectral image classification to improve the classification accuracy. This book has promoted the application of graph neural network in hyperspectral image classification, providing reference for remote sensing image processing. It will be a useful reference for researchers in remote sensing image processing and image neural network design.