A Generative Approach Towards Improved Robotic Detection of Marine Litter
This paper presents an approach to address data scarcity problems in underwater image datasets for visual detection of marine debris. The proposed approach relies on a two-stage variational autoencoder (VAE) and a binary classifier to evaluate the generated imagery for quality and realism. From the...
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| creator | Hong, Jungseok Fulton, Michael Sattar, Junaed |
| description | This paper presents an approach to address data scarcity problems in
underwater image datasets for visual detection of marine debris. The proposed
approach relies on a two-stage variational autoencoder (VAE) and a binary
classifier to evaluate the generated imagery for quality and realism. From the
images generated by the two-stage VAE, the binary classifier selects "good
quality" images and augments the given dataset with them. Lastly, a multi-class
classifier is used to evaluate the impact of the augmentation process by
measuring the accuracy of an object detector trained on combinations of real
and generated trash images. Our results show that the classifier trained with
the augmented data outperforms the one trained only with the real data. This
approach will not only be valid for the underwater trash classification problem
presented in this paper, but it will also be useful for any data-dependent task
for which collecting more images is challenging or infeasible. |
| doi_str_mv | 10.48550/arxiv.1910.04754 |
| format | Article |
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underwater image datasets for visual detection of marine debris. The proposed
approach relies on a two-stage variational autoencoder (VAE) and a binary
classifier to evaluate the generated imagery for quality and realism. From the
images generated by the two-stage VAE, the binary classifier selects "good
quality" images and augments the given dataset with them. Lastly, a multi-class
classifier is used to evaluate the impact of the augmentation process by
measuring the accuracy of an object detector trained on combinations of real
and generated trash images. Our results show that the classifier trained with
the augmented data outperforms the one trained only with the real data. This
approach will not only be valid for the underwater trash classification problem
presented in this paper, but it will also be useful for any data-dependent task
for which collecting more images is challenging or infeasible.</description><identifier>DOI: 10.48550/arxiv.1910.04754</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Robotics</subject><creationdate>2019-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1910.04754$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1910.04754$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Hong, Jungseok</creatorcontrib><creatorcontrib>Fulton, Michael</creatorcontrib><creatorcontrib>Sattar, Junaed</creatorcontrib><title>A Generative Approach Towards Improved Robotic Detection of Marine Litter</title><description>This paper presents an approach to address data scarcity problems in
underwater image datasets for visual detection of marine debris. The proposed
approach relies on a two-stage variational autoencoder (VAE) and a binary
classifier to evaluate the generated imagery for quality and realism. From the
images generated by the two-stage VAE, the binary classifier selects "good
quality" images and augments the given dataset with them. Lastly, a multi-class
classifier is used to evaluate the impact of the augmentation process by
measuring the accuracy of an object detector trained on combinations of real
and generated trash images. Our results show that the classifier trained with
the augmented data outperforms the one trained only with the real data. This
approach will not only be valid for the underwater trash classification problem
presented in this paper, but it will also be useful for any data-dependent task
for which collecting more images is challenging or infeasible.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Computer Science - Robotics</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz0FLwzAYxvFcPMjmB_BkvkBn3jZZmmOZOgsdgvRe0uR9MbA1JQtVv71zenrgf3jgx9g9iI2slRKPNn2FZQPmEoTUSt6ytuF7nDDZHBbkzTynaN0H7-OnTf7M29MlLOj5exxjDo4_YUaXQ5x4JH6wKUzIu5AzpjW7IXs8493_rlj_8tzvXovubd_umq6wWy0L8kbWpFCb0iirdTkaoW2FxgOBr1VliOrRkSnBkSAQ5agJwIktgpBQVyv28Hd7tQxzCiebvodf03A1VT-zZEaM</recordid><startdate>20191010</startdate><enddate>20191010</enddate><creator>Hong, Jungseok</creator><creator>Fulton, Michael</creator><creator>Sattar, Junaed</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20191010</creationdate><title>A Generative Approach Towards Improved Robotic Detection of Marine Litter</title><author>Hong, Jungseok ; Fulton, Michael ; Sattar, Junaed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-fd948f5e79295a772b907a3e9d1f1d8539ff8bcf921cf0f102b7f11c06e104183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Computer Science - Robotics</topic><toplevel>online_resources</toplevel><creatorcontrib>Hong, Jungseok</creatorcontrib><creatorcontrib>Fulton, Michael</creatorcontrib><creatorcontrib>Sattar, Junaed</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hong, Jungseok</au><au>Fulton, Michael</au><au>Sattar, Junaed</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Generative Approach Towards Improved Robotic Detection of Marine Litter</atitle><date>2019-10-10</date><risdate>2019</risdate><abstract>This paper presents an approach to address data scarcity problems in
underwater image datasets for visual detection of marine debris. The proposed
approach relies on a two-stage variational autoencoder (VAE) and a binary
classifier to evaluate the generated imagery for quality and realism. From the
images generated by the two-stage VAE, the binary classifier selects "good
quality" images and augments the given dataset with them. Lastly, a multi-class
classifier is used to evaluate the impact of the augmentation process by
measuring the accuracy of an object detector trained on combinations of real
and generated trash images. Our results show that the classifier trained with
the augmented data outperforms the one trained only with the real data. This
approach will not only be valid for the underwater trash classification problem
presented in this paper, but it will also be useful for any data-dependent task
for which collecting more images is challenging or infeasible.</abstract><doi>10.48550/arxiv.1910.04754</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition Computer Science - Robotics |
| title | A Generative Approach Towards Improved Robotic Detection of Marine Litter |
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