METHOD AND SYSTEM FOR URBAN ROAD INFRASTRUCTURE MONITORING
Methods and systems for monitoring infrastructure, can involve capturing video of infrastructure, and generating an inference of damage to the infrastructure and a severity thereof based on images in the captured video and in response to running the inference locally on one or more edge devices. The running of the inference can take place locally on the edge device(s) using a compression of models to run the inference on the at edge device(s) with a low computational resource. Privacy preserved learning can be enabled when generating the inference and the severity thereof by using distributed data subject to federated learning frameworks.
1 . A method of monitoring infrastructure, comprising:
capturing video of infrastructure; and
generating an inference of damage to the infrastructure and a severity thereof based on images in the captured video and in response to running the inference locally on at least one edge device.
2 . The method of claim 1 wherein the running of the inference locally on the at least one edge device comprises using a compression of models to run the inference on the at least one edge device with a low computational resource.
3 . The method of claim 1 enabling privacy preserved learning when generating the inference and the severity thereof by using distributed data subject to at least one federated learning framework.
4 . The method of claim 1 wherein the at least one edge device includes a camera mounted on at least one vehicle of a public transportation fleet of vehicles.
5 . The method of claim 4 further comprising capturing the location of the damage based on a position of the at least one vehicle.
6 . The method of claim 1 further comprising displaying data indicative of the inference of damage to the infrastructure in a cartographic display.
7 . The method of claim 1 further comprising: distributing training data among a plurality of clients, wherein the training data utilized in generating the inference of damage.
8 . A system for monitoring infrastructure, comprising:
at least one image-capturing device for capturing video of infrastructure; and
at least one edge device that communicates with the at least one image-capturing device, wherein an inference of damage to the infrastructure and a severity thereof based on images in the captured video are generated in response to running the inference locally on the at least one edge device.
9 . The system of claim 8 wherein the running of the inference locally on the at least one edge device comprises using a compression of models to run the inference on the at least one edge device with a low computational resource.
10 . The system of claim 8 wherein privacy preserved learning is enabled when generating the inference and the severity thereof by using distributed data subject to at least one federated learning framework.
11 . The system of claim 8 wherein the at least one edge device is associated with the at least one image-capturing device mounted on at least one vehicle of a public transportation fleet of vehicles.
12 . The system of claim 11 further wherein the location of the damage is captured by the at least one image-capturing device based on a position of the at least one vehicle.
13 . The system of claim 8 further comprising a cartographic display for displaying data indicative of the inference of damage to the infrastructure.
14 . The system of claim 8 wherein training data is distributed among a plurality of clients, the training data utilized in generating the inference of damage.
15 . A system of monitoring infrastructure, comprising:
at least one processor and a memory, the memory storing instructions to cause the at least one processor to perform:
capturing video of infrastructure; and
generating an inference of damage to the infrastructure and a severity thereof based on images in the captured video and in response to running the inference locally on at least one edge device.
16 . The system of claim 14 wherein the instructions are further configured to cause the at least one processor to perform: running of the inference locally on the at least one edge device comprises using a compression of models to run the inference on the at least one edge device with a low computational resource.
17 . The system of claim 14 wherein the instructions are further configured to cause the at least one processor to perform: enabling privacy preserved learning when generating the inference and the severity thereof by using distributed data subject to at least one federated learning framework.
18 . The system of claim 14 wherein the at least one edge device includes a camera mounted on at least one vehicle of a public transportation fleet of vehicles.
19 . The system of claim 18 wherein the instructions are further configured to cause the at least one processor to perform: capturing the location of the damage based on a position of the at least one vehicle.
20 . The system of claim 14 wherein the instructions are further configured to cause the at least one processor to perform: displaying data indicative of the inference of damage to the infrastructure in a cartographic display.