Physics aware Self supervised Training of CNNs for Object Detection
Impressive progress has been achieved recently in object detection with the use of deep learning. Nevertheless, such tools typically require large amounts of training data and significant manual effort for labeling objects. This limits their applicability in realistic domains, where it is necessary to scale solutions to a large number of objects and a variety of conditions. This work proposes a training process for Convolutional Neural Networks (CNNs) which minimizes manual effort and takes advantage of physical reasoning through the use of a physicsengine. The application involves detection of objects placed in cluttered scenes and tight environments, such as shelves. In particular, given access to 3D object models, the labeling takes place automatically over synthetic images, which correspond to physically realistic object poses. To further improve object detection, the network self trains over real images that contain the target objects. In these situations, successful detections can be used to provide additional real world examples. Then, the trained network can be successfully applied in complicated scenes, which involve clutter and object occlusions, without manual labeling. The proposed training process is evaluated by showing that it can match the accuracy of a training process based on manual labeling and can improve over time through selftraining resulting in a lifelong learning solution.