Targeted Improvement of a Deep Learning Object Detector Using Synthetic Training Data
When working with object detection, the quality and quantity of the training data is often a recurrent bottleneck. This thesis proposes a technique of incrementally improving an object detector using synthetically rendered data. The current training data within the field of focus is limited, and creating new data comes with significant integrity and security risks. By creating synthetic images whe