Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914162272108544 |
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| author | Ren, Jintao Bi, Ziqian Niu, Qian Song, Xinyuan Jiang, Zekun Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Wang, Jinlang Chen, Keyu Yin, Caitlyn Heqi Feng, Pohsun Wen, Yizhu Wang, Tianyang Chen, Silin Li, Ming Xu, Jiawei Liu, Ming |
| author_facet | Ren, Jintao Bi, Ziqian Niu, Qian Song, Xinyuan Jiang, Zekun Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Wang, Jinlang Chen, Keyu Yin, Caitlyn Heqi Feng, Pohsun Wen, Yizhu Wang, Tianyang Chen, Silin Li, Ming Xu, Jiawei Liu, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15584 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications Ren, Jintao Bi, Ziqian Niu, Qian Song, Xinyuan Jiang, Zekun Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Wang, Jinlang Chen, Keyu Yin, Caitlyn Heqi Feng, Pohsun Wen, Yizhu Wang, Tianyang Chen, Silin Li, Ming Xu, Jiawei Liu, Ming Computer Vision and Pattern Recognition Graphics 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. |
| title | Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2410.15584 |