Thermal Brake Disc Fissures

The core hypothesis of this research is that integrating localized object detection (YOLOv8m) with fine-grained semantic segmentation (U-Net with a ResNet50 backbone) on thermal infrared imagery significantly enhances the accuracy and reliability of crack detection in railway brake discs compared to conventional visual inspection or single-model AI approaches. Thermal imaging provides superior contrast for identifying heat dissipation patterns around metallic fissures, which are often invisible to the naked eye under industrial conditions. This dataset contains a comprehensive collection of thermal images (160 x 120 px resolution) captured from railway brake discs during preventive maintenance. The data includes: Original Thermal Frames which are raw captures of brake discs showing various thermal signatures. Annotated Masks: Ground-truth labels for three types of cracks: Penetrating, Incipient, and Superficial. Augmented Samples: Images processed with noise injection and geometric transformations to ensure model robustness.