Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications

• URL: http://arxiv.org/abs/2410.15584v1
• Date: Mon, 21 Oct 2024 02:10:49 GMT
• Title: Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
• Authors: Jintao Ren, Ziqian Bi, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Yizhu Wen, Tianyang Wang, Silin Chen, Ming Li, Jiawei Xu, Ming Liu,
• Abstract summary: Book covers state-of-the-art advancements in machine learning and deep learning. Focuses on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches like DETR. Book also delves into the integration of artificial intelligence (AI) techniques and large language models for enhanced object detection.
• Score: 17.571124565519263
• License:
• Abstract: This book offers an in-depth exploration of object detection and semantic segmentation, combining theoretical foundations with practical applications. It covers state-of-the-art advancements in machine learning and deep learning, with a focus on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches like DETR. The book also delves into the integration of artificial intelligence (AI) techniques and large language models for enhanced object detection in complex environments. A thorough discussion of big data analysis is presented, highlighting the importance of data processing, model optimization, and performance evaluation metrics. By bridging the gap between traditional methods and modern deep learning frameworks, this book serves as a comprehensive guide for researchers, data scientists, and engineers aiming to leverage AI-driven methodologies in large-scale object detection tasks.

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