Skip to content
Tutorials3 min read

为目标检测绘制边界框

完整的边界框标注设置指南，适用于计算机视觉任务，包括标签颜色和验证规则。

Potato Team·

为目标检测绘制边界框

边界框标注是训练目标检测模型的基础。本教程涵盖从基本设置到预标注和验证规则等高级功能的全部内容。

基本边界框设置

最简配置

yaml
annotation_task_name: "Object Detection Annotation"
 
data_files:
  - "data/images.json"
 
annotation_schemes:
  - annotation_type: image_annotation
    name: objects
    description: "Draw boxes around all objects"
    tools:
      - bbox
    labels:
      - name: car
        color: "#FF6B6B"
      - name: person
        color: "#4ECDC4"
      - name: bicycle
        color: "#45B7D1"

工作流程

  1. 从工具栏选择标签
  2. 点击并拖动绘制框
  3. 调整角点以精确框选
  4. 根据需要添加更多框
  5. 完成后提交

详细标签配置

yaml
annotation_schemes:
  - annotation_type: image_annotation
    name: objects
    description: "Annotate all visible objects"
    tools:
      - bbox
    labels:
      - name: person
        color: "#FF6B6B"
        description: "Any human, partial or full"
        keyboard_shortcut: "p"
 
      - name: car
        color: "#4ECDC4"
        description: "Cars, trucks, SUVs"
        keyboard_shortcut: "c"
 
      - name: motorcycle
        color: "#45B7D1"
        description: "Motorcycles and scooters"
        keyboard_shortcut: "m"
 
      - name: bicycle
        color: "#96CEB4"
        description: "Bicycles of all types"
        keyboard_shortcut: "b"
 
      - name: traffic_light
        color: "#FFEAA7"
        description: "Traffic signals"
        keyboard_shortcut: "t"
 
      - name: stop_sign
        color: "#DDA0DD"
        description: "Stop signs"
        keyboard_shortcut: "s"

添加目标属性

为每个框捕获额外信息：

yaml
annotation_schemes:
  - annotation_type: image_annotation
    name: objects
    tools:
      - bbox
    labels:
      - name: person
        color: "#FF6B6B"
        attributes:
          - name: occlusion
            type: radio
            options: [none, partial, heavy]
          - name: truncated
            type: checkbox
            description: "Object extends beyond image"
          - name: difficult
            type: checkbox
            description: "Hard to identify"

当标注者绘制框时，系统会提示他们填写这些属性。

验证规则

通过验证确保标注质量：

yaml
annotation_schemes:
  - annotation_type: image_annotation
    name: objects
    tools:
      - bbox
    labels: [...]
    min_annotations: 1

键盘快捷键

Potato 包含内置键盘快捷键以提高标注效率：

  • 数字键选择标签
  • Delete 键删除选中的标注
  • 方向键在项目间导航

显示选项

配置图像显示设置：

yaml
image_display:
  width: 800
  height: 600

完整的生产配置

yaml
annotation_task_name: "Autonomous Driving - Object Detection"
 
data_files:
  - "data/driving_frames.json"
 
annotation_schemes:
  - annotation_type: image_annotation
    name: objects
    description: "Annotate all traffic participants and objects"
    tools:
      - bbox
    min_annotations: 1
    labels:
      - name: vehicle
        color: "#FF6B6B"
        keyboard_shortcut: "v"
        attributes:
          - name: type
            type: radio
            options: [car, truck, bus, motorcycle, bicycle]
          - name: occlusion
            type: radio
            options: [0%, 1-25%, 26-50%, 51-75%, 76-99%]
 
      - name: pedestrian
        color: "#4ECDC4"
        keyboard_shortcut: "p"
        attributes:
          - name: pose
            type: radio
            options: [standing, walking, sitting, lying]
          - name: age_group
            type: radio
            options: [child, adult, elderly]
 
      - name: cyclist
        color: "#45B7D1"
        keyboard_shortcut: "c"
 
      - name: traffic_sign
        color: "#FFEAA7"
        keyboard_shortcut: "t"
        attributes:
          - name: sign_type
            type: radio
            options: [stop, yield, speed_limit, warning, other]
 
      - name: traffic_light
        color: "#DDA0DD"
        keyboard_shortcut: "l"
        attributes:
          - name: state
            type: radio
            options: [red, yellow, green, off, unknown]

输出格式

json
{
  "frame_id": "frame_0001",
  "frame_path": "/images/frame_0001.jpg",
  "image_dimensions": {"width": 1920, "height": 1080},
  "annotations": {
    "objects": [
      {
        "label": "vehicle",
        "bbox": [450, 380, 680, 520],
        "attributes": {
          "type": "car",
          "occlusion": "0%"
        }
      },
      {
        "label": "pedestrian",
        "bbox": [820, 400, 870, 550],
        "attributes": {
          "pose": "walking",
          "age_group": "adult"
        }
      }
    ]
  }
}

高质量边界框技巧

  1. 紧凑的框：最小化框中的背景
  2. 一致的规则：清晰记录边缘情况
  3. 校准会议：团队一起审查示例
  4. 跟踪指标：监控框的数量和大小分布

下一步

完整文档请参阅 /docs/features/image-annotation。