Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/136904
Title: Evaluation and comparison of various deep neural networks for monocular depth estimation
Authors: Zhang, Ziyi
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2019
Publisher: Nanyang Technological University
Project: A1247-182
Abstract: In this final year project, several testing scenarios and related methodology have been designed to examine the performance of the cutting-edge neural networks for monocular depth estimation. Since neural networks for monocular depth estimation is a fast-developing and emerging research field in recent years, neural network design and techniques involved keep evolving. It is both reasonable and beneficial to perceive different novel network design and implement these networks personally. If all the parameters during testing meet the lowest expectations in relative real-life application scenarios, it can be expected that neural networks will replace the dedicated depth sensors and make a huge difference in high-tech fields like artificial intelligence and autonomous driving.
URI: https://hdl.handle.net/10356/136904
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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