Ongoing projects in our group
Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have however relied upon massive amounts of human annotated training data. This time-consuming process has begun impeding the progress of these deep learning efforts. By training machine learning algorithms on a rich virtual world, we can illustrate that real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning’s application to sensor based classification problems like those that appear in self-driving cars.
One of the major open challenges in self-driving cars is the ability to detect cars and pedestrians to safely navigate in the world. Deep learning-based object detector approaches have enabled great advances in using camera imagery to detect and classify objects. But for a safety critical application such as autonomous driving, the error rates of the current state-of-the-art are still too high to enable safe operation. Errors that occur on novel data go undetected without additional human labels. In this paper, we propose an automated method to identify mistakes made by object detectors without ground truth labels. The proposed method achieves over 97% precision in automatically identifying missed detections produced by one of the leading state-of-the-art object detectors in the literature.