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

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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