Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
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.
Cite
@article{arxiv.2410.15584,
title = {Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications},
author = {Jintao Ren and Ziqian Bi and Qian Niu and Xinyuan Song and Zekun Jiang and Junyu Liu and Benji Peng and Sen Zhang and Xuanhe Pan and Jinlang Wang and Keyu Chen and Caitlyn Heqi Yin and Pohsun Feng and Yizhu Wen and Tianyang Wang and Silin Chen and Ming Li and Jiawei Xu and Ming Liu},
journal= {arXiv preprint arXiv:2410.15584},
year = {2025}
}
Comments
167 pages