MODNet: A Monocular Object-Based Depth Estimation Network for AI Robotic Chemists

With the rapid development of AI chemists, the application of machine-learning methods has brought transformative changes to the field of chemistry. Computer vision technology is also gradually becoming a critical sensory input for AI-driven chemists. However, for a complete AI chemist platform, int...

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Bibliographic Details
Main Author:	Xun Fu (2081062) (author)
Other Authors:	Xiaogang Cheng (8166234) (author), Yugang Chen (2358241) (author), Haopeng Wang (1770376) (author), Lizhu Dong (20723609) (author), Xinyao Ge (22765475) (author), Yongxia Wang (436910) (author), Ying Wei (214901) (author), Linghai Xie (1550764) (author)
Published:	2025
Subjects:	Biochemistry Pharmacology Ecology Inorganic Chemistry Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified unit operations within original yolov8 algorithm cvig_ii data set critical sensory input conducted validation experiments brought transformative changes also gradually becoming chemical laboratory environments computer vision technology autonomous chemical synthesis modnet auxiliary system 9 ′- xanthene accurate object detection researching machine vision ai robotic chemists chemical apparatus synthesis process monocular object precision detection driven chemists distance detection ai chemists work focuses video streams time guidance rapid development paper proposes model combined learning methods error margin enabling rapid assisted solution 60 fps
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MODNet: A Monocular Object-Based Depth Estimation Network for AI Robotic Chemists

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