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.
