<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-crack-segmentation-dataset.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Crack Segmentation Dataset In Colab"></a>
The [Roboflow](https://roboflow.com/?ref=ultralytics) [Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) 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](https://www.ultralytics.com/glossary/computer-vision-cv) applications for recreational purposes.
<strong>Watch:</strong> Crack segmentation using Ultralytics YOLOv9
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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](https://www.ultralytics.com/glossary/accuracy) of your self-driving car models, this dataset provides a rich and varied collection of images to support your endeavors.