Fast Road Detection by CNN-Based Camera-Lidar Fusion and Spherical Coordinate Transformation
We propose a new camera-lidar fusion method for road detection where the spherical coordinate transformation is introduced to decrease the gap between the point cloud of 3D lidar data. The camera's color data and the 3D lidar's height data are transformed into the same spherical coordinate...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2021-09, Vol.22 (9), p.5802-5810 |
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| creator | Lee, Jae-Seol Park, Tae-Hyoung |
| description | We propose a new camera-lidar fusion method for road detection where the spherical coordinate transformation is introduced to decrease the gap between the point cloud of 3D lidar data. The camera's color data and the 3D lidar's height data are transformed into the same spherical coordinate, and then input to the convolution neural network for segmentation. Faster segmentation is possible due to the reduced size of input data. To increase the detection accuracy, this modified SegNet expands the receptive field of the network. Using the KITTI dataset, we present the experimental results to show the usefulness of the proposed method. |
| doi_str_mv | 10.1109/TITS.2020.2988302 |
| format | Article |
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The camera's color data and the 3D lidar's height data are transformed into the same spherical coordinate, and then input to the convolution neural network for segmentation. Faster segmentation is possible due to the reduced size of input data. To increase the detection accuracy, this modified SegNet expands the receptive field of the network. Using the KITTI dataset, we present the experimental results to show the usefulness of the proposed method.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2020.2988302</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Autonomous vehicles ; Cameras ; convolution neural network ; Convolutional neural networks ; Coordinate transformations ; Image segmentation ; Laser radar ; Lidar ; lidar and camera fusion ; Road detection ; Segmentation ; Sensor fusion ; spherical coordinate transformation ; Spherical coordinates ; Three dimensional models</subject><ispartof>IEEE transactions on intelligent transportation systems, 2021-09, Vol.22 (9), p.5802-5810</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Using the KITTI dataset, we present the experimental results to show the usefulness of the proposed method.</description><subject>Artificial neural networks</subject><subject>Autonomous vehicles</subject><subject>Cameras</subject><subject>convolution neural network</subject><subject>Convolutional neural networks</subject><subject>Coordinate transformations</subject><subject>Image segmentation</subject><subject>Laser radar</subject><subject>Lidar</subject><subject>lidar and camera fusion</subject><subject>Road detection</subject><subject>Segmentation</subject><subject>Sensor fusion</subject><subject>spherical coordinate transformation</subject><subject>Spherical coordinates</subject><subject>Three dimensional models</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWD9-gHgJeN46ySZpctTVaqFUsOtNCGk-cEu7qcn20H_vLi2e5mV43hl4ELojMCYE1GM9q5djChTGVElZAj1DI8K5LACIOB8yZYUCDpfoKud1v2WckBH6nprc4c9oHH7xnbddE1u8OuBqsSieTfYOV2brkynmjTMJT_d5AEzr8HL341NjzQZXMSbXtKbzuE6mzSGmrRkO3aCLYDbZ357mNfqavtbVezH_eJtVT_PCUlV2hS0lU-VKlMYqB0pwwZwLnIeJhAkDHpxRYBX1RFFquS8ZZRMaBLhJ6IMsr9HD8e4uxd-9z51ex31q-5eaciFBKCWgp8iRsinmnHzQu9RsTTpoAnqQqAeJepCoTxL7zv2x03jv_3kFkhEO5R9i2WwW</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Lee, Jae-Seol</creator><creator>Park, Tae-Hyoung</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-3695-344X</orcidid><orcidid>https://orcid.org/0000-0002-8615-171X</orcidid></search><sort><creationdate>20210901</creationdate><title>Fast Road Detection by CNN-Based Camera-Lidar Fusion and Spherical Coordinate Transformation</title><author>Lee, Jae-Seol ; Park, Tae-Hyoung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-c38493b63ac9d096564ddf55f7807405fda90c92e1922c5e342472f60d7f47283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Autonomous vehicles</topic><topic>Cameras</topic><topic>convolution neural network</topic><topic>Convolutional neural networks</topic><topic>Coordinate transformations</topic><topic>Image segmentation</topic><topic>Laser radar</topic><topic>Lidar</topic><topic>lidar and camera fusion</topic><topic>Road detection</topic><topic>Segmentation</topic><topic>Sensor fusion</topic><topic>spherical coordinate transformation</topic><topic>Spherical coordinates</topic><topic>Three dimensional models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Jae-Seol</creatorcontrib><creatorcontrib>Park, Tae-Hyoung</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lee, Jae-Seol</au><au>Park, Tae-Hyoung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fast Road Detection by CNN-Based Camera-Lidar Fusion and Spherical Coordinate Transformation</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2021-09-01</date><risdate>2021</risdate><volume>22</volume><issue>9</issue><spage>5802</spage><epage>5810</epage><pages>5802-5810</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>We propose a new camera-lidar fusion method for road detection where the spherical coordinate transformation is introduced to decrease the gap between the point cloud of 3D lidar data. 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| ispartof | IEEE transactions on intelligent transportation systems, 2021-09, Vol.22 (9), p.5802-5810 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Artificial neural networks Autonomous vehicles Cameras convolution neural network Convolutional neural networks Coordinate transformations Image segmentation Laser radar Lidar lidar and camera fusion Road detection Segmentation Sensor fusion spherical coordinate transformation Spherical coordinates Three dimensional models |
| title | Fast Road Detection by CNN-Based Camera-Lidar Fusion and Spherical Coordinate Transformation |
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