This project focuses on using defect detection probability within CT (Computed Tomography) images as a metric for assessing image quality. Key steps include:
- Establishing a data preparation pipeline to insert defects into CT volumes sourced from CAD files.
- Simulating CT scans to replicate imaging processes.
- Developing a defect detection neural network to analyze CT images and determine the probability of defect presence.
- Utilizing the defect detection probability as a quantitative metric for evaluating the quality of CT images, with potential integration of trajectory optimization techniques.