| CPC H04W 4/40 (2018.02) [G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 20/588 (2022.01)] | 20 Claims |

|
1. A method of detecting road anomalies, the method comprising:
obtaining visual data of a road from an end computing device in a vehicle travelling along the road, wherein the visual data is a live feed captured using a camera mounted in or on the vehicle and sent to the end computing device;
inputting the visual data to a machine learning (ML) model in the end computing device that learns to detect and classify at least one road anomaly into a class including one of a pothole, a longitudinal crack, a transverse crack, or an alligator crack on the road;
outputting the class and a bounding box, created by the ML model, of the at least one road anomaly;
determining multiple post-detection features of the detected at least one road anomaly, wherein the determining multiple post-detection features includes determining that the at least one road anomaly is detected across multiple sequential frames;
when the at least one road anomaly is detected across multiple sequential frames, determining a number of skip frames based on a model fidelity distance (MFD) and a vehicle speed, wherein the MFD is a distance that the ML model can detect objects within a predetermined error;
determining that the detected at least one road anomaly is detected multiple times in each of the multiple sequential frames;
skipping frames based on the number of skip frames to a key frame; and
transmitting, via a roadside computing device installed proximate to the road, a notification to multiple end computing devices in a communication range of the roadside computing device, wherein the notification is an anomaly-specific notification that includes at least one of a message indicating a presence of the road anomaly, an image of the road anomaly, a location of the road anomaly, or a severity of the road anomaly.
|