Abstract: A method and system for dimension estimation based on duplication identification is disclosed. In some embodiments, the method includes receiving a set of images of an object. The method includes detecting, from each image in the set of images, a respective image segmentation representing a damage of the object. The method then includes determining a respective dimension for the damage represented by each of the image segmentations. The method further includes determining whether two or more of the image segmentations represent a same damage of the object. Responsive to two or more of the image segmentations representing a same damage of the object, the method includes combining the respective dimensions determined for the damage represented by the two or more image segmentations to obtain a final dimension for the same damage.

Abstract: A method and system for dimension estimation based on duplication identification is disclosed. In some embodiments, the method includes receiving a set of images of an object. The method includes detecting, from each image in the set of images, a respective image segmentation representing a damage of the object. The method then includes determining a respective dimension for the damage represented by each of the image segmentations. The method further includes determining whether two or more of the image segmentations represent a same damage of the object. Responsive to two or more of the image segmentations representing a same damage of the object, the method includes combining the respective dimensions determined for the damage represented by the two or more image segmentations to obtain a final dimension for the same damage.

Abstract: A method for dimension estimation based on duplication identification. In some embodiments, the method includes receiving a set of images of an object. The method then includes detecting, using a first machine learning system trained to perform image segmentation, a first image segmentation representing a damage of the object on a first image and a second image segmentation representing a damage of the object on a second image. The method further includes determining, using a second machine learning system trained to perform dimension estimation, a first dimension for the damage represented by the first image segmentation and a second dimension for the damage represented by the second image segmentation. The method includes determining whether the first and second image segmentations represent a same damage. If these image segmentations represent the same damage, the method intelligently combines the first and second dimensions to obtain a final dimension for the damage.

Abstract: A method for dimension estimation based on duplication identification. In some embodiments, the method includes receiving a set of images of an object. The method then includes detecting, using a first machine learning system trained to perform image segmentation, a first image segmentation representing a damage of the object on a first image and a second image segmentation representing a damage of the object on a second image. The method further includes determining, using a second machine learning system trained to perform dimension estimation, a first dimension for the damage represented by the first image segmentation and a second dimension for the damage represented by the second image segmentation. The method includes determining whether the first and second image segmentations represent a same damage. If these image segmentations represent the same damage, the method intelligently combines the first and second dimensions to obtain a final dimension for the damage.

Abstract: Systems and methods for redundancy in a network device are disclosed. An exemplary network device comprises: a plurality of data forwarding elements (DFEs); and a redundant control plane. The redundant control plane comprises: an active control processor for configuring forwarding operation of each of the DFEs; an active layer-2 switch coupled to the active control processor and to each of the DFEs; a standby control processor; and a standby layer-2 switch coupled to the standby control processor and to each of the DFEs. The active control processor is programmed in a full-mesh so that the active control processor is in communication with each of the DFEs. The standby control processor is programmed in a full-mesh so that the standby control processor is in communication with each of the DFEs.

Abstract: Systems and methods for routing media packets between a plurality of processors and a digital signal processor (DSP) are disclosed. An exemplary device comprises: an egress processor; an ingress processor; conversion logic; and a DSP configured to transcode a payload in the packet. The ingress processor is configured to change a first layer-2 header of a received media packet to a second layer-2 header. The second layer-2 header includes a DSP address and an egress processor address. The first layer-2 header has a first format and the second layer-2 header having a second different format. The conversion logic is configured to change the second layer-2 header to a third layer-2 header, and to change the third layer-2 header to a fourth layer-2 header including the egress processor address in the first format. The third layer-2 header includes the egress processor address in the first format.

Abstract: Systems and methods for redundancy in a network device are disclosed. An exemplary device comprises: a controller residing on a data forwarding element (DFE), the DFE performing data forwarding operations; and a first and a second control processor. Each of the control processors has either an active role or a standby role with respect to the DFE controller. The active-role control processor communicates with the DFE to configure the DFE forwarding operations and receives at least some communications from the DFE controller over a virtual channel. The DFE controller directs traffic on the virtual channel that is destined for the active-role control processor to either the first or the second control processor based on which of the control processors currently has the active role.