Towards a Generic Object Detection Algorithm

Abstract

In the field of Autonomous Robotics, object detection is a key element for a robot to interact with the world. A robot must be aware of its surroundings in order to take the appropriate action for those surroundings. Through extensive research, algorithms to detect objects and determine their location in images have been developed, however, these algorithms require a large amount of high quality images of the objects. These images must be annotated, requiring a human to explicitly state where an object is in an image. For several objects, images are not available, and there are simply too many objects in our world to effectively detect all of them. We put forward a method of detecting objects without a significant number of images, dubbed Zero Shot Detection. This method functions by creating binary feature extractors and using the output of several feature extractors to create a vector representation of an object. This vector representation is then matched against a list of several objects that were not in the original dataset and their expected output from those feature extractors to find the closest match. This match is the classification of that object.