Interactive Training of Object Detection Without ImageNet

Abstract

For many robotic tasks, particularly those of service robots operating in human environments, the scope of object detection needs is greater than the available data. Either public datasets do not contain the entire set of objects needed for the task, and/or it is a commercial application that cannot use public datasets for training. Instead of hiring people to hand-label more data to support the integration of new objects into robot perception, we propose an interactive training process requiring zero hand labeling. With as little as 4 minutes of interaction with the robot per object, we demonstrate 99% precision and 57% recall in stationary object detection tasks.