Abstract: Environmental tracking systems and methods are disclosed. An environmental tracking system receives sensor data from the one or more sensors, such as camera(s) and Light Detection and Ranging (LIDAR) sensors. The system uses trained machine learning (ML) model(s) to detect, within the sensor data, representation(s) of at least a portion of a vehicle with a door that is at least partially open. Based on these representation(s), the system generates a boundary for the vehicle that includes the door and is sized based on the door being at least partially open. The system determines a route that avoids the boundary, for example by planning the route around the boundary or by planning to stop before intersecting with the boundary. In some examples, the sensors are sensors coupled to a second vehicle, and the second vehicle traverses the route.

Abstract: Techniques for optimizing vehicle license plate recognition in images and their decoding include training a set of convolutional neural networks (CNNs) by using images in which license plates are identified or labeled as a whole, rather than by license plate parts or key points, and rather than by the individual, segmented characters represented thereon. The trained CNNs may operate on target images of environments to localize images of license plates included therein and determine the issuing jurisdiction and/or ordered set of characters represented on detected license plates without utilizing character segmentation and/or per-character recognition techniques. As such, license plates depicted within target images are able to be detected and decoded with greater tolerances for lighting conditions, deformations or damages, occlusions, differing image resolutions, differing angles of capture, variations of other objects depicted within the images (such as dense or changing signage), etc.

Abstract: Methods and systems of annotation densification for semantic segmentation are disclosed herein. In one example embodiment, such a method includes obtaining image information, obtaining coarse annotation information, performing an image matting operation based upon the image information and based at least indirectly upon the coarse annotation information, and applying an already-trained Convolutional Neural Network (ConvNet) semantic segmentation model in relation to the image information.

Abstract: Methods and systems of annotation densification for semantic segmentation are disclosed herein. In one example embodiment, such a method includes obtaining image information, obtaining coarse annotation information, performing an image matting operation based upon the image information and based at least indirectly upon the coarse annotation information, and applying an already-trained Convolutional Neural Network (ConvNet) semantic segmentation model in relation to the image information.

Abstract: Embodiments of process kit shields and process chambers incorporating same are provided herein. In some embodiments, a one-piece process kit shield includes a cylindrical body having an upper portion and a lower portion; a heat transfer channel extending through the upper portion; and a cover ring section extending radially inward from the lower portion.

Abstract: Methods and systems involving convolutional neural networks as applicable for semantic segmentation, including multi-task convolutional networks employing curriculum based transfer learning, are disclosed herein. In one example embodiment, a method of semantic segmentation involving a convolutional neural network includes training and applying the convolutional neural network. The training of the convolutional neural network includes each of training a semantic segmentation decoder network of the convolutional neural network, generating first feature maps by way of an encoder network of the convolutional neural network, based at least in part upon a dataset received at the encoder network, and training an instance segmentation decoder network of the convolutional neural network based at least in part upon the first feature maps.

Abstract: Embodiments of process kit shields and process chambers incorporating same are provided herein. In some embodiments, a one-piece process kit shield includes a cylindrical body having an upper portion and a lower portion; a heat transfer channel extending through the upper portion; and a cover ring section extending radially inward from the lower portion.