Crack Segmentation Using YOLOv11 - Complete Tutorial

Learn to implement crack segmentation using YOLOv11 in this comprehensive Python tutorial that demonstrates training and testing a custom image segmentation model for defect detection. Follow a complete step-by-step workflow covering installation of required libraries including Ultralytics, OpenCV, and NumPy, followed by proper data preparation techniques for crack segmentation datasets. Build and train a YOLOv11 model from scratch on custom crack data, then run inference on test images to evaluate performance. Master the extraction, visualization, and saving of predicted segmentation masks, and discover how to compare predicted results against ground truth masks using OpenCV for validation. Gain practical experience with semantic segmentation applied to real-world material analysis and defect detection scenarios, with hands-on coding examples that can be adapted for similar computer vision projects involving structural damage assessment or quality control applications.