Developing an Object Detector Using Synthetic Data from CAD Models

This work investigates the potential of using synthetic images generated from CAD models to train an object detector for identifying components of a particle accelerator. The study focuses on magnets within the new ALS Accumulator Ring at Lawrence Berkeley National Laboratory. Generating large volumes of real-world training data is often challenging in such complex systems. To address this, CAD files were converted into 3D models and used to produce diverse synthetic datasets. These datasets were augmented with a smaller set of real-world images to train a YOLOv8-based model. This approach aims to evaluate whether synthetic images can effectively support the development of object detectors in environments where real data collection is limited. The study lays the groundwork for future development of real-time recognition tools to assist accelerator operations.