{"id":46759,"date":"2021-05-11T12:34:02","date_gmt":"2021-05-11T11:34:02","guid":{"rendered":"https:\/\/irap.org\/?p=46759"},"modified":"2021-10-12T01:52:37","modified_gmt":"2021-10-12T00:52:37","slug":"omdena-challenge-collaborators-share-results","status":"publish","type":"post","link":"https:\/\/irap.org\/vi\/2021\/05\/omdena-challenge-collaborators-share-results\/","title":{"rendered":"C\u1ed9ng t\u00e1c vi\u00ean Omdena Challenge chia s\u1ebb k\u1ebft qu\u1ea3"},"content":{"rendered":"

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Collaborators on the Omdena iRAP 8 Week Challenge<\/a> which aimed to crowd source AI-based solutions to map crash risk and generate iRAP star rating attribute data have shared some of their publication results.<\/p>\n

Earlier this year, 50 AI and machine learning experts including aspiring students, data scientists, ML and data engineers, and domain experts from around the world worked with members of the iRAP Innovation and Development Team focussed on finding a solution for the accelerated and intelligent collection and coding of road attribute data (AiRAP) to prevent road crashes and save lives.<\/p>\n

Read these insightful and well applied Python methods:<\/p>\n

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Using Convolutional Neural Networks To Improve Road Safety And Save\u00a0Lives” by\u00a0Lois Anne Leal<\/a>\u00a0and\u00a0Chaitree Baradkar<\/a> – https:\/\/bit.ly\/3gK8men<\/a><\/p>\n

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“Feature Analysis and Machine Learning for Road\u00a0Safety” by\u00a0Leon Hamnett<\/a>
https:\/\/bit.ly\/3gJGuqt<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.9.7″ _module_preset=”default”][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2021\/07\/Feature.jpg” title_text=”Feature” _builder_version=”4.9.7″ _module_preset=”default”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”2_5,3_5″ _builder_version=”4.9.7″ _module_preset=”default”][et_pb_column type=”2_5″ _builder_version=”4.9.7″ _module_preset=”default”][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2021\/07\/bend13-678×675-1-1.jpg” title_text=”bend13-678×675 (1)” _builder_version=”4.9.7″ _module_preset=”default”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.9.7″ _module_preset=”default”][et_pb_text _builder_version=”4.9.7″ _module_preset=”default”]<\/p>\n

“Object Detection in 2D images to locate cars and pedestrians” by\u00a0Giulio Giaconi<\/a>\u00a0and\u00a0Dave Bunten<\/a>
https:\/\/bit.ly\/3vce0cV<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.9.7″ _module_preset=”default”][et_pb_column type=”3_4″ _builder_version=”4.9.7″ _module_preset=”default”][et_pb_text _builder_version=”4.9.7″ _module_preset=”default”]<\/p>\n

“Advanced EDA of United Kingdom\u2019s Road Safety Data using\u00a0Python” by\u00a0Nabanita Roy<\/a>
https:\/\/bit.ly\/3dY8l4v<\/a><\/p>\n

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“Streamlit 101: Deploying an AutoML Streamlit Dashboard to Predict and Show Results” by\u00a0MUHAMMAD ALI<\/a>
https:\/\/bit.ly\/3vov9QP<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.9.7″ _module_preset=”default”][et_pb_column type=”3_4″ _builder_version=”4.9.7″ _module_preset=”default”][et_pb_text _builder_version=”4.9.7″ _module_preset=”default”]<\/p>\n

“Learning OpenCV from Scratch to Build a Pedestrian Detector” by\u00a0Rutuja Kawade<\/a>
https:\/\/bit.ly\/2PsvPoS<\/a><\/p>\n

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iRAP extends its sincere thanks to the Omdena team. The results will inform big steps forward for #3starorbetter roads for all road users that will save lives.\u00a0<\/p>\n

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Collaborators on the Omdena iRAP 8 Week Challenge which aimed to crowd source AI-based solutions to map crash risk and generate iRAP star rating attribute data have shared some of […]<\/p>\n","protected":false},"author":21,"featured_media":46765,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"

Collaborators on the Omdena iRAP 8 Week Challenge<\/a> which aimed to crowd source AI-based solutions to map crash risk and generate iRAP star rating attribute data have shared some of their publication results.<\/p>

Earlier this year, 50 AI and machine learning experts including aspiring students, data scientists, ML and data engineers, and domain experts from around the world worked with members of the iRAP Innovation and Development Team focussed on finding a solution for the accelerated and intelligent collection and coding of road attribute data (AiRAP) to prevent road crashes and save lives.<\/p>

Read these insightful and well applied Python methods:<\/p>","_et_gb_content_width":"","_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"footnotes":""},"categories":[624,1,24],"tags":[353,540,72,355],"class_list":["post-46759","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-airap","category-general","category-news","tag-airap","tag-challenge","tag-innovation","tag-omdena"],"yoast_head":"\nOmdena Challenge collaborators share results - iRAP<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/irap.org\/vi\/2021\/05\/omdena-challenge-collaborators-share-results\/\" \/>\n<meta property=\"og:locale\" content=\"vi_VN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Omdena Challenge collaborators share results - 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