{"id":1961,"date":"2022-03-08T15:19:05","date_gmt":"2022-03-08T15:19:05","guid":{"rendered":"https:\/\/metricsproject.eu\/?page_id=1961"},"modified":"2024-09-25T15:25:28","modified_gmt":"2024-09-25T15:25:28","slug":"2022-cascade-campaign","status":"publish","type":"page","link":"https:\/\/metricsproject.eu\/agri-food\/2022-cascade-campaign\/","title":{"rendered":"2022 Cascade Campaign"},"content":{"rendered":"<div id=\"pl-1961\"  class=\"panel-layout\" ><div id=\"pg-1961-0\"  class=\"panel-grid panel-no-style\" ><div id=\"pgc-1961-0-0\"  class=\"panel-grid-cell panel-grid-cell-empty panel-grid-cell-mobile-last\" ><\/div><div id=\"pgc-1961-0-1\"  class=\"panel-grid-cell panel-grid-cell-empty\" ><\/div><\/div><div id=\"pg-1961-1\"  class=\"panel-grid panel-no-style\" ><div id=\"pgc-1961-1-0\"  class=\"panel-grid-cell\" ><div id=\"panel-1961-1-0-0\" class=\"so-panel widget widget_sow-editor panel-first-child panel-last-child\" data-index=\"0\" ><div\n\t\t\t\n\t\t\tclass=\"so-widget-sow-editor so-widget-sow-editor-base\"\n\t\t\t\n\t\t>\n<div class=\"siteorigin-widget-tinymce textwidget\">\n\t<h2 class=\"p4\">2022 ACRE Cascade Campaign<\/h2>\n<p>In the\u00a0<strong>2022 ACRE 1st Cascade Campaign<\/strong>, participants were asked to segment RGB images to distinguish between crop, weeds, and background.<\/p>\n<p>Data has been collected in real crop fields during 2019 and 2021 agricultural robotics competitions. The dataset comprises images captured by two agricultural robots in different moments and with different RGB cameras. Images consist of two kinds of crops (maize and bean) and multiple species of weeds. Participants are provided with labeled images to train their models, and they are asked to submit their hypothesis (segmented images) of a test dataset.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>The competition was structured into three stages:<\/strong><\/p>\n<ol>\n<li><strong>Development<\/strong>: in this stage, participants are asked to develop a model to perform semantic segmentation of RGB images by training their models on the 2019 dataset.<\/li>\n<li><strong>Generalization<\/strong>: in this stage, participants are asked to submit predictions of the unlabelled 2021 dataset by using their models trained on the 2019 dataset. Different environmental conditions and sensors\u2019 settings require the models to have generalization capability. The generalization capability can be reached by applying for style transfer and\/or domain adaptation techniques.<\/li>\n<li><strong>Final<\/strong>: in this stage, participants are required to submit predictions of a new unlabelled 2021 test set. This stage is thought to submit the final model without major changes; thus, the duration is limited to three days and the number of submissions to three.<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<p><strong>Competition timeline:<\/strong><\/p>\n<p>Stage 1 - Development opens: 23rd February 2022<\/p>\n<p>Stage 2 - Generalization opens: 6th April 2022<\/p>\n<p>Stage 3 - Final opens: 12nd May 2022<\/p>\n<p>Stage 3 - Final closes: 15th May 2022<\/p>\n<p>Algorithm submission deadline: 17th May 2022<\/p>\n<p>Final rank: 23-27th May 2022<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Results:<\/strong><\/p>\n<p>We are pleased to announce our winners:<\/p>\n<p>Congratulations to our winner - <strong>Mengru Ma!<\/strong><\/p>\n<p>Additionally we wanted to highlight the Maize winner - <strong>Zhi Li.<\/strong><\/p>\n<p>&nbsp;<\/p>\n<\/div>\n<\/div><\/div><\/div><div id=\"pgc-1961-1-1\"  class=\"panel-grid-cell\" ><div id=\"panel-1961-1-1-0\" class=\"so-panel widget widget_sow-editor panel-first-child panel-last-child\" data-index=\"1\" ><div class=\"panel-widget-style panel-widget-style-for-1961-1-1-0\" ><div\n\t\t\t\n\t\t\tclass=\"so-widget-sow-editor so-widget-sow-editor-base\"\n\t\t\t\n\t\t>\n<div class=\"siteorigin-widget-tinymce textwidget\">\n\t<h3><strong>More information:\u00a0<\/strong><\/h3>\n<p><a href=\"mailto:acre@metricsproject.eu\">acre(a)metricsproject.eu<\/a><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>2022 ACRE Cascade Campaign In the\u00a02022 ACRE 1st Cascade Campaign, participants were asked to segment RGB images to distinguish between crop, weeds, and background. Data has been collected in real crop fields during 2019 and 2021 agricultural robotics competitions. The dataset comprises images captured by two agricultural robots in different moments and with different RGB [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":1662,"parent":98,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"templates\/agri-page.php","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages\/1961"}],"collection":[{"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/comments?post=1961"}],"version-history":[{"count":31,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages\/1961\/revisions"}],"predecessor-version":[{"id":3418,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages\/1961\/revisions\/3418"}],"up":[{"embeddable":true,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages\/98"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/media\/1662"}],"wp:attachment":[{"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/media?parent=1961"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}