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.

<strong>关于本数据集的详细说明</strong>可参考<strong>ACRE_datasheet.md</strong>文件，该说明依据<em>《数据集说明书（Datasheets for Datasets）》</em>（Gebru, Timnit 等. 《Datasheets for datasets》. <em>ACM通讯（Communications of the ACM）</em>, 2021, 64(12): 86-92.）。<br><strong>本数据集的创建目的是什么？</strong><br>ACRE数据集是在METRICS项目框架下构建的，旨在作为杂草检测模型的基准测试集，可应用于目标检测（object detection）、语义分割（semantic segmentation）、实例分割（instance segmentation）等多种任务。农业食品机器人评估竞赛（Agri-Food Competition for Robot Evaluation, ACRE）是专为自主机器人与智能作业设备设计的基准测试竞赛，核心聚焦于除草、田间导航等农业作业场景，相关能力对推动数字农业（Digital Agriculture）转型至关重要。ACRE竞赛隶属于METRICS项目——这是一项由欧盟资助的自主机器人计量学评估与测试专项，其官方页面可通过https://metricsproject.eu/agri-food 访问。<br><strong>数据集中的样本代表了什么内容？</strong><br>数据样本为同时包含作物与杂草的RGB图像。作物类别涵盖两个物种：玉米（Zea mays）与菜豆（Phaseolus vulgaris）；杂草类别则包含四个物种：多年生黑麦草（Lolium perenne）、野芥（Sinapis arvensis）、母菊（Matricaria chamomilla）以及藜（Chenopodium album）。<br><strong>每个样本是否带有标注或目标标签？</strong><br>数据集中的每张图像均配有包含实例分割标注的XML文件。<br><strong>数据采集采用了何种机制或流程？</strong><br>数据采集使用了一台四轮滑移转向机器人，搭载Basler acA2000-50gc型RGB相机。相机安装于机器人上，其光轴垂直指向地面，分辨率为2046×1080像素。机器人采用远程操控模式，平均运行速度为0.2 m/s。采集过程中，相机的视频流以rosbag格式进行录制，相机通过以太网接口连接至运行Ubuntu 18.04与ROS Melodic的个人计算机。