canola_detection_dataset.zip

RGB images were obtained from three distinct datasets featuring canola in its early growth stage intermingled with weeds, including <b>T1 Miling</b> (T1_miling), <b>T2 Miling</b> (T2_Miling), and <b>York canola</b> (YC). These datasets were collected in Western Australia from distances ranging between 0.5 meters and 1.5 meters, captured at various angles. The images are processed as 500 by 500 pixel frames, comprising the core images along with their corresponding segmentation masks. Manual delineation of bounding boxes using the polygon tool was performed using the Make Sense annotation tool by Skalski, P. [GitHub: https://github.com/SkalskiP/make-sense/].For T1 and T2 Millet datasets, the images showcase canola plants in conjunction with rye grass, whereas the York canola dataset features images of canola alongside regrowth of blue lupin. Image collection involved the use of a smartphone for T1 and T2 and a Canon 600D DSLR camera for YC. If you are interested in the scripts for image processing and deep learning, they can be accessed at [GitHub: https://github.com/mikemcka/Canola_detection_dl/tree/main].In this repository you will find the RGB images and weed-crop-soil segmentation masks obtained using CIVE vegetation index.RGB图像源自三个包含早期生长阶段油菜与杂草混生场景的独立数据集，分别为T1 Miling（T1_miling）、T2 Miling（T2_Miling）以及York油菜（YC）。这些数据集采集于西澳大利亚地区，拍摄距离介于0.5米至1.5米之间，且采用了多样化的拍摄角度。所有图像均被统一处理为500×500像素的帧，包含原始RGB图像及其对应的分割掩码（segmentation masks）。标注工作由Skalski, P.借助Make Sense标注工具（Make Sense annotation tool）完成，具体通过多边形工具（polygon tool）手动绘制边界框（bounding boxes），该工具的开源仓库可访问[GitHub：https://github.com/SkalskiP/make-sense/]。针对T1和T2 Miling数据集，其图像展示了与黑麦草伴生的油菜植株；而York油菜数据集的图像则呈现了与再生蓝羽扇豆伴生的油菜场景。图像采集环节中，T1与T2数据集采用智能手机拍摄，YC数据集则使用佳能600D单反相机完成拍摄。若您需要获取图像处理与深度学习相关的脚本代码，可访问[GitHub：https://github.com/mikemcka/Canola_detection_dl/tree/main]。该仓库中包含了通过CIVE植被指数（CIVE vegetation index）提取得到的RGB图像与杂草-作物-土壤分割掩码。