Benchmarking convolutional neural networks for object segmentation and pose estimation

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Le, Tiffany Anh Mai

Advisor dc:contributor.advisor

• Lei Hamilton abd Antonio Torralba.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119531

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119531