Benchmarking convolutional neural networks for object segmentation and pose estimation
Name
1066740404-MIT.pdf
Description
Full printable version
Size
7.82 MB
Format
Adobe PDF
Checksum (MD5)
21ef5dc6875ddb56a162e01c16f2bbaf
Author(s)
Le, Tiffany Anh Mai
Advisor(s)
Lei Hamilton abd Antonio Torralba.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Convolutional neural networks (CNNs), particularly those designed for object segmentation and pose estimation, are now applied to robotics applications involving mobile manipulation. For these robotic applications to be successful, robust and accurate performance from the CNNs is critical. Therefore, in order to develop an understanding of CNN performance, several CNN architectures are benchmarked on a set of metrics for object segmentation and pose estimation. This thesis presents these benchmarking results, which show that metric performance is dependent on the complexity of network architectures. The reasons behind poor pose estimation and object segmentation, which include object symmetry and resolution loss due to downsampling followed by upsampling in the networks, respectively, are also identified in this thesis. These findings can be used to guide and improve the development of CNNs for object segmentation and pose estimation in the future.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-60).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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