Roboflow 宇宙裂缝分割数据集

The Roboflow Crack Segmentation Dataset stands out as an extensive resource designed specifically for individuals involved in transportation and public safety studies. It is equally beneficial for those working on the development of self-driving car models or simply exploring computer vision applications for recreational purposes.

Comprising a total of 4029 static images captured from diverse road and wall scenarios, this dataset emerges as a valuable asset for tasks related to crack segmentation. Whether you are delving into the intricacies of transportation research or seeking to enhance the accuracy of your self-driving car models, this dataset provides a rich and varied collection of images to support your endeavors.

数据集结构

裂缝分割数据集的数据划分概述如下：

• 训练集：由 3717 幅图像和相应的注释组成。
• 测试集：由 112 幅图像及其注释组成。
• 验证集：包括 200 幅图像及其相应的注释。

应用

裂缝分割技术可实际应用于基础设施维护，帮助识别和评估结构性损坏。它还能使自动系统检测和处理路面裂缝，以便及时维修，从而在提高道路安全方面发挥重要作用。

数据集 YAML

使用 YAML（另一种标记语言）文件概述数据集的配置，包括路径、类和其他相关信息的详细信息。具体来说，对于裂缝分割数据集，YAML 文件中的 crack-seg.yaml 文件由 https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/crack-seg.yaml.

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Crack-seg dataset by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/segment/crack-seg/
# Example usage: yolo train data=crack-seg.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── crack-seg  ← downloads here (91.2 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/crack-seg # dataset root dir
train: train/images # train images (relative to 'path') 3717 images
val: valid/images # val images (relative to 'path') 112 images
test: test/images # test images (relative to 'path') 200 images

# Classes
names:
  0: crack

# Download script/URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/crack-seg.zip

使用方法

To train Ultralytics YOLO11n model on the Crack Segmentation dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="crack-seg.yaml", epochs=100, imgsz=640)

# Start training from a pretrained *.pt model
yolo segment train data=crack-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

样本数据和注释

裂缝分割数据集包含从多个角度拍摄的各种图像和视频。以下是数据集中的数据实例，并附有各自的注释：

• 这幅图像展示了图像对象分割的一个示例，其特点是注释了边界框，并用遮罩勾勒出识别出的对象。该数据集包括在不同地点、环境和密度下拍摄的各种图像，是开发针对这一特定任务的模型的综合资源。

• 这个例子突出了裂缝分割数据集的多样性和复杂性，强调了高质量数据在计算机视觉任务中的关键作用。

引文和致谢

如果您在研究或开发工作中使用裂缝分割数据集，请参考以下论文：

@misc{ crack-bphdr_dataset,
    title = { crack Dataset },
    type = { Open Source Dataset },
    author = { University },
    howpublished = { \url{ https://universe.roboflow.com/university-bswxt/crack-bphdr } },
    url = { https://universe.roboflow.com/university-bswxt/crack-bphdr },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { dec },
    note = { visited on 2024-01-23 },
}

Roboflow 团队创建并维护了裂缝分割数据集，为道路安全和研究项目提供了宝贵的资源，在此表示感谢。有关裂缝分割数据集及其创建者的更多信息，请访问裂缝分割数据集页面。

常见问题

什么是Roboflow 裂缝分割数据集？

Roboflow 裂缝分割数据集是一个包含 4029 幅静态图像的综合数据集，专为交通和公共安全研究而设计。它是自动驾驶汽车模型开发和基础设施维护等任务的理想选择。该数据集包括训练集、测试集和验证集，有助于进行准确的裂缝检测和分割。

How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLO11?

To train an Ultralytics YOLO11 model on the Crack Segmentation dataset, use the following code snippets. Detailed instructions and further parameters can be found on the model Training page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="crack-seg.yaml", epochs=100, imgsz=640)

# Start training from a pretrained *.pt model
yolo segment train data=crack-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

为什么要在自动驾驶汽车项目中使用裂缝分割数据集？

裂缝分割数据集收集了 4029 幅道路和墙壁图像，提供了各种不同的场景，因此非常适合自动驾驶汽车项目。这种多样性提高了为裂缝检测而训练的模型的准确性和鲁棒性，而裂缝检测对于维护道路安全和确保基础设施的及时维修至关重要。

Ultralytics YOLO 在裂缝分割方面有哪些独特功能？

Ultralytics YOLO offers advanced real-time object detection, segmentation, and classification capabilities that make it ideal for crack segmentation tasks. Its ability to handle large datasets and complex scenarios ensures high accuracy and efficiency. For example, the model Training, Predict, and Export modes cover comprehensive functionalities from training to deployment.

如何在研究论文中引用Roboflow 裂缝分割数据集？

如果您在研究中使用了裂缝分割数据集，请使用以下 BibTeX 参考文献：

@misc{ crack-bphdr_dataset,
    title = { crack Dataset },
    type = { Open Source Dataset },
    author = { University },
    howpublished = { \url{ https://universe.roboflow.com/university-bswxt/crack-bphdr } },
    url = { https://universe.roboflow.com/university-bswxt/crack-bphdr },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { dec },
    note = { visited on 2024-01-23 },
}

这种引用格式可确保对数据集创建者的适当认可，并确认其在研究中的使用。

📅 Created 9 months ago ✏️ Updated 23 days ago

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