The ACRE Crop-Weed Dataset

<strong>For a detailed description of this dataset</strong>, based on the <em>Datasheets for Datasets</em> (Gebru, Timnit, et al. "Datasheets for datasets." <em>Communications of the ACM</em> 64.12 (2021): 86-92.), check the <strong>ACRE_datasheet.md</strong> file. <strong>For what purpose was the dataset created?</strong><br> The ACRE dataset was created within the scope of the METRICS project to serve as a benchmark for weed detection models in various tasks, including object detection, semantic segmentation, and instance segmentation. The Agri-Food Competition for Robot Evaluation (ACRE) is a benchmarking competition specifically designed for autonomous robots and smart implements, with a primary focus on agricultural activities like weed removal and field navigation. These capabilities play a vital role in facilitating the transition to Digital Agriculture. The ACRE competition, which can be found at https://metricsproject.eu/agri-food, is part of the METRICS project, an EU-funded initiative dedicated to the metrological evaluation and testing of autonomous robots. <strong>What do the instances that comprise the dataset represent?</strong><br> The instances consist of RGB images depicting both crop and weed plants. The crop category encompasses two species: maize (Zea mays) and beans (Phaseolus vulgaris). On the other hand, the weed category encompasses four species: ryegrass (Lolium perenne), mustard (Sinapis arvensis), matricaria (Matricaria chamomilla), and lamb's quarter (Chenopodium album). <strong>Is there a label or target associated with each instance?</strong><br> Every image in the dataset is accompanied by an XML file that contains instance segmentation annotations. <strong>What mechanisms or procedures were used to collect the data?</strong><br> The data collection process involved the use of a four-wheel skid-steering robot that was equipped with a Basler acA2000-50gc RGB camera. The camera was mounted on the robot in such a way that its principal axis was directed perpendicular to the ground. It had a resolution of 2046 x 1080 pixels. The robot was teleoperated and operated at an average speed of 0.2 m/s. To capture the data, the camera's stream was recorded in rosbag format. For this purpose, the camera was connected to a PC running Ubuntu 18.04 and ROS Melodic via an Ethernet interface.