使用深度学习目标检测框架yolov8训练车站行为摔倒袭击异常行为检测数据集构建车站行为异常检测系统识别摔打袭击破坏公共物品包括数据集准备、模型训练和结果评估车站行为异常检测数据集2295张、yolo和voc两种标注方式4类标注数量 NORMAL正常:2770FALLDOWN摔倒:1210ASSAULT袭击:794VANDALISM破坏公共物品:86image num:2295基于车站行为异常检测数据集进行目标检测任务。数据集包含2295张图片并且标注为YOLO和VOC两种格式。以下是详细的步骤包括数据准备、模型训练和评估。文章代码及内容仅供参考环境准备确保您已经安装了以下软件和库Python 3.8 或更高版本PyTorch 1.9 或更高版本torchvision 0.10 或更高版本OpenCVnumpypandasmatplotlibscikit-imagealbumentations用于数据增强您可以使用以下命令安装所需的Python库pipinstalltorch torchvision opencv-python numpy pandas matplotlib scikit-image albumentations ultralytics数据集准备假设您的数据集已经按照标准格式组织好并且包含训练集、验证集和测试集。以下是数据集的预期结构datasets/ └── station_behavior_detection/ ├── images/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels_yolo/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels_voc/ │ ├── Annotations/ │ ├── ImageSets/ │ └── Main/ └── data.yaml类别描述文件 (data.yaml)根据提供的信息整理出以下类别描述文件[titleYOLOv8 Data Configuration File]train:../datasets/station_behavior_detection/images/train/val:../datasets/station_behavior_detection/images/val/nc:4# number of classesnames:[NORMAL,FALLDOWN,ASSAULT,VANDALISM]# list of class names训练代码我们将使用YOLOv8进行训练。首先克隆YOLOv8仓库并设置环境。gitclone https://github.com/ultralytics/ultralytics.gitcdultralytics pipinstall-rrequirements.txt准备配置文件我们已经在上一步中创建了data.yaml文件。训练模型使用以下命令开始训练yolotaskdetectmodetrainmodelyolov8n.ptdata../datasets/station_behavior_detection/data.yamlepochs100imgsz640batch16结果评估训练完成后可以使用以下命令评估模型性能yolotaskdetectmodevalmodelruns/detect/train/weights/best.ptdata../datasets/station_behavior_detection/data.yaml使用说明配置路径确保datasets/station_behavior_detection/目录结构正确。确保data.yaml中的路径和类别名称正确。运行脚本在终端中依次运行训练脚本和评估脚本。注意事项根据需要调整超参数和训练设置。可以通过修改model参数来选择不同的YOLOv8模型架构如yolov8s.pt,yolov8m.pt,yolov8l.pt,yolov8x.pt。示例假设您的数据集文件夹结构如下datasets/ └── station_behavior_detection/ ├── images/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels_yolo/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels_voc/ │ ├── Annotations/ │ ├── ImageSets/ │ └── Main/ └── data.yaml并且images/目录包含所有的图片labels_yolo/目录包含对应的TXT标注文件。运行上述脚本后您可以查看训练日志和最终的模型权重文件。转换VOC标注到YOLO标注如果您的数据集同时提供了VOC格式的标注但您希望统一使用YOLO格式可以编写一个脚本来转换标注格式。[titleConvert VOC to YOLO Format]importosimportxml.etree.ElementTreeasETimportcv2defconvert_voc_to_yolo(voc_annotations_dir,yolo_labels_dir,image_dir):# Create the output directory if it doesnt existos.makedirs(yolo_labels_dir,exist_okTrue)# Get all annotation files in the directoryannotation_files[fforfinos.listdir(voc_annotations_dir)iff.endswith(.xml)]forann_fileinannotation_files:treeET.parse(os.path.join(voc_annotations_dir,ann_file))roottree.getroot()image_filenameroot.find(filename).text image_pathos.path.join(image_dir,image_filename)imagecv2.imread(image_path)height,width,_image.shapewithopen(os.path.join(yolo_labels_dir,ann_file.replace(.xml,.txt)),w)asf:forobjinroot.findall(object):label_nameobj.find(name).text bboxobj.find(bndbox)xminint(bbox.find(xmin).text)yminint(bbox.find(ymin).text)xmaxint(bbox.find(xmax).text)ymaxint(bbox.find(ymax).text)# Convert bounding box coordinates from Pascal VOC format to YOLO formatx_center(xminxmax)/2.0/width y_center(yminymax)/2.0/height bbox_width(xmax-xmin)/width bbox_height(ymax-ymin)/height# Map label name to class IDclass_id_map{NORMAL:0,FALLDOWN:1,ASSAULT:2,VANDALISM:3}class_idclass_id_map[label_name]f.write(f{class_id}{x_center}{y_center}{bbox_width}{bbox_height}\n)# Example usagevoc_annotations_dir../datasets/station_behavior_detection/labels_voc/Annotationsyolo_labels_dir../datasets/station_behavior_detection/labels_yoloimage_dir../datasets/station_behavior_detection/imagesconvert_voc_to_yolo(voc_annotations_dir,yolo_labels_dir,image_dir)总结可以构建一个全面的车站行为异常检测系统包括数据集准备、模型训练和结果评估。以下是所有相关的代码文件YOLOv8数据集配置文件(data.yaml)训练命令VOC到YOLO格式转换脚本希望这些信息对你有所帮助如果您有任何问题或需要进一步的帮助请告诉我。完整代码总结以下是完整的配置文件和训练命令YOLOv8数据集配置文件 (data.yaml)[titleYOLOv8 Data Configuration File]train:../datasets/station_behavior_detection/images/train/val:../datasets/station_behavior_detection/images/val/nc:4# number of classesnames:[NORMAL,FALLDOWN,ASSAULT,VANDALISM]# list of class names训练命令yolotaskdetectmodetrainmodelyolov8n.ptdata../datasets/station_behavior_detection/data.yamlepochs100imgsz640batch16评估命令yolotaskdetectmodevalmodelruns/detect/train/weights/best.ptdata../datasets/station_behavior_detection/data.yamlVOC到YOLO格式转换脚本[titleConvert VOC to YOLO Format]importosimportxml.etree.ElementTreeasETimportcv2defconvert_voc_to_yolo(voc_annotations_dir,yolo_labels_dir,image_dir):# Create the output directory if it doesnt existos.makedirs(yolo_labels_dir,exist_okTrue)# Get all annotation files in the directoryannotation_files[fforfinos.listdir(voc_annotations_dir)iff.endswith(.xml)]forann_fileinannotation_files:treeET.parse(os.path.join(voc_annotations_dir,ann_file))roottree.getroot()image_filenameroot.find(filename).text image_pathos.path.join(image_dir,image_filename)imagecv2.imread(image_path)height,width,_image.shapewithopen(os.path.join(yolo_labels_dir,ann_file.replace(.xml,.txt)),w)asf:forobjinroot.findall(object):label_nameobj.find(name).text bboxobj.find(bndbox)xminint(bbox.find(xmin).text)yminint(bbox.find(ymin).text)xmaxint(bbox.find(xmax).text)ymaxint(bbox.find(ymax).text)# Convert bounding box coordinates from Pascal VOC format to YOLO formatx_center(xminxmax)/2.0/width y_center(yminymax)/2.0/height bbox_width(xmax-xmin)/width bbox_height(ymax-ymin)/height# Map label name to class IDclass_id_map{NORMAL:0,FALLDOWN:1,ASSAULT:2,VANDALISM:3}class_idclass_id_map[label_name]f.write(f{class_id}{x_center}{y_center}{bbox_width}{bbox_height}\n)# Example usagevoc_annotations_dir../datasets/station_behavior_detection/labels_voc/Annotationsyolo_labels_dir../datasets/station_behavior_detection/labels_yoloimage_dir../datasets/station_behavior_detection/imagesconvert_voc_to_yolo(voc_annotations_dir,yolo_labels_dir,image_dir)hope这些信息能帮助您顺利进行模型训练和评估。
使用深度学习目标检测框架yolov8训练车站行为摔倒袭击异常行为检测数据集构建车站行为异常检测系统识别摔打袭击破坏公共物品包括数据集准备、模型训练和结果评估车站行为异常检测数据集2295张、yolo和voc两种标注方式4类标注数量 NORMAL正常:2770FALLDOWN摔倒:1210ASSAULT袭击:794VANDALISM破坏公共物品:86image num:2295基于车站行为异常检测数据集进行目标检测任务。数据集包含2295张图片并且标注为YOLO和VOC两种格式。以下是详细的步骤包括数据准备、模型训练和评估。文章代码及内容仅供参考环境准备确保您已经安装了以下软件和库Python 3.8 或更高版本PyTorch 1.9 或更高版本torchvision 0.10 或更高版本OpenCVnumpypandasmatplotlibscikit-imagealbumentations用于数据增强您可以使用以下命令安装所需的Python库pipinstalltorch torchvision opencv-python numpy pandas matplotlib scikit-image albumentations ultralytics数据集准备假设您的数据集已经按照标准格式组织好并且包含训练集、验证集和测试集。以下是数据集的预期结构datasets/ └── station_behavior_detection/ ├── images/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels_yolo/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels_voc/ │ ├── Annotations/ │ ├── ImageSets/ │ └── Main/ └── data.yaml类别描述文件 (data.yaml)根据提供的信息整理出以下类别描述文件[titleYOLOv8 Data Configuration File]train:../datasets/station_behavior_detection/images/train/val:../datasets/station_behavior_detection/images/val/nc:4# number of classesnames:[NORMAL,FALLDOWN,ASSAULT,VANDALISM]# list of class names训练代码我们将使用YOLOv8进行训练。首先克隆YOLOv8仓库并设置环境。gitclone https://github.com/ultralytics/ultralytics.gitcdultralytics pipinstall-rrequirements.txt准备配置文件我们已经在上一步中创建了data.yaml文件。训练模型使用以下命令开始训练yolotaskdetectmodetrainmodelyolov8n.ptdata../datasets/station_behavior_detection/data.yamlepochs100imgsz640batch16结果评估训练完成后可以使用以下命令评估模型性能yolotaskdetectmodevalmodelruns/detect/train/weights/best.ptdata../datasets/station_behavior_detection/data.yaml使用说明配置路径确保datasets/station_behavior_detection/目录结构正确。确保data.yaml中的路径和类别名称正确。运行脚本在终端中依次运行训练脚本和评估脚本。注意事项根据需要调整超参数和训练设置。可以通过修改model参数来选择不同的YOLOv8模型架构如yolov8s.pt,yolov8m.pt,yolov8l.pt,yolov8x.pt。示例假设您的数据集文件夹结构如下datasets/ └── station_behavior_detection/ ├── images/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels_yolo/ │ ├── train/ │ ├── val/ │ └── test/ ├── labels_voc/ │ ├── Annotations/ │ ├── ImageSets/ │ └── Main/ └── data.yaml并且images/目录包含所有的图片labels_yolo/目录包含对应的TXT标注文件。运行上述脚本后您可以查看训练日志和最终的模型权重文件。转换VOC标注到YOLO标注如果您的数据集同时提供了VOC格式的标注但您希望统一使用YOLO格式可以编写一个脚本来转换标注格式。[titleConvert VOC to YOLO Format]importosimportxml.etree.ElementTreeasETimportcv2defconvert_voc_to_yolo(voc_annotations_dir,yolo_labels_dir,image_dir):# Create the output directory if it doesnt existos.makedirs(yolo_labels_dir,exist_okTrue)# Get all annotation files in the directoryannotation_files[fforfinos.listdir(voc_annotations_dir)iff.endswith(.xml)]forann_fileinannotation_files:treeET.parse(os.path.join(voc_annotations_dir,ann_file))roottree.getroot()image_filenameroot.find(filename).text image_pathos.path.join(image_dir,image_filename)imagecv2.imread(image_path)height,width,_image.shapewithopen(os.path.join(yolo_labels_dir,ann_file.replace(.xml,.txt)),w)asf:forobjinroot.findall(object):label_nameobj.find(name).text bboxobj.find(bndbox)xminint(bbox.find(xmin).text)yminint(bbox.find(ymin).text)xmaxint(bbox.find(xmax).text)ymaxint(bbox.find(ymax).text)# Convert bounding box coordinates from Pascal VOC format to YOLO formatx_center(xminxmax)/2.0/width y_center(yminymax)/2.0/height bbox_width(xmax-xmin)/width bbox_height(ymax-ymin)/height# Map label name to class IDclass_id_map{NORMAL:0,FALLDOWN:1,ASSAULT:2,VANDALISM:3}class_idclass_id_map[label_name]f.write(f{class_id}{x_center}{y_center}{bbox_width}{bbox_height}\n)# Example usagevoc_annotations_dir../datasets/station_behavior_detection/labels_voc/Annotationsyolo_labels_dir../datasets/station_behavior_detection/labels_yoloimage_dir../datasets/station_behavior_detection/imagesconvert_voc_to_yolo(voc_annotations_dir,yolo_labels_dir,image_dir)总结可以构建一个全面的车站行为异常检测系统包括数据集准备、模型训练和结果评估。以下是所有相关的代码文件YOLOv8数据集配置文件(data.yaml)训练命令VOC到YOLO格式转换脚本希望这些信息对你有所帮助如果您有任何问题或需要进一步的帮助请告诉我。完整代码总结以下是完整的配置文件和训练命令YOLOv8数据集配置文件 (data.yaml)[titleYOLOv8 Data Configuration File]train:../datasets/station_behavior_detection/images/train/val:../datasets/station_behavior_detection/images/val/nc:4# number of classesnames:[NORMAL,FALLDOWN,ASSAULT,VANDALISM]# list of class names训练命令yolotaskdetectmodetrainmodelyolov8n.ptdata../datasets/station_behavior_detection/data.yamlepochs100imgsz640batch16评估命令yolotaskdetectmodevalmodelruns/detect/train/weights/best.ptdata../datasets/station_behavior_detection/data.yamlVOC到YOLO格式转换脚本[titleConvert VOC to YOLO Format]importosimportxml.etree.ElementTreeasETimportcv2defconvert_voc_to_yolo(voc_annotations_dir,yolo_labels_dir,image_dir):# Create the output directory if it doesnt existos.makedirs(yolo_labels_dir,exist_okTrue)# Get all annotation files in the directoryannotation_files[fforfinos.listdir(voc_annotations_dir)iff.endswith(.xml)]forann_fileinannotation_files:treeET.parse(os.path.join(voc_annotations_dir,ann_file))roottree.getroot()image_filenameroot.find(filename).text image_pathos.path.join(image_dir,image_filename)imagecv2.imread(image_path)height,width,_image.shapewithopen(os.path.join(yolo_labels_dir,ann_file.replace(.xml,.txt)),w)asf:forobjinroot.findall(object):label_nameobj.find(name).text bboxobj.find(bndbox)xminint(bbox.find(xmin).text)yminint(bbox.find(ymin).text)xmaxint(bbox.find(xmax).text)ymaxint(bbox.find(ymax).text)# Convert bounding box coordinates from Pascal VOC format to YOLO formatx_center(xminxmax)/2.0/width y_center(yminymax)/2.0/height bbox_width(xmax-xmin)/width bbox_height(ymax-ymin)/height# Map label name to class IDclass_id_map{NORMAL:0,FALLDOWN:1,ASSAULT:2,VANDALISM:3}class_idclass_id_map[label_name]f.write(f{class_id}{x_center}{y_center}{bbox_width}{bbox_height}\n)# Example usagevoc_annotations_dir../datasets/station_behavior_detection/labels_voc/Annotationsyolo_labels_dir../datasets/station_behavior_detection/labels_yoloimage_dir../datasets/station_behavior_detection/imagesconvert_voc_to_yolo(voc_annotations_dir,yolo_labels_dir,image_dir)hope这些信息能帮助您顺利进行模型训练和评估。
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