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

• URL: http://arxiv.org/abs/2410.15584v2
• Date: Wed, 18 Dec 2024 05:13:44 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: In-depth exploration of object detection and semantic segmentation is provided. State-of-the-art advancements in machine learning and deep learning are reviewed. Analysis of big data processing is presented.
• Score: 17.571124565519263
• License:
• Abstract: An in-depth exploration of object detection and semantic segmentation is provided, combining theoretical foundations with practical applications. State-of-the-art advancements in machine learning and deep learning are reviewed, focusing on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches such as DETR. The integration of artificial intelligence (AI) techniques and large language models for enhancing object detection in complex environments is examined. Additionally, a comprehensive analysis of big data processing is presented, with emphasis on model optimization and performance evaluation metrics. By bridging the gap between traditional methods and modern deep learning frameworks, valuable insights are offered for researchers, data scientists, and engineers aiming to apply AI-driven methodologies to large-scale object detection tasks.

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