# US 12,215,466 B1 · Airport pavement condition assessment methods and apparatuses

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- Number: US 12,215,466 B1
- Document ID: US12215466B1
- Status: Granted
- Kind: B1
- Application number: 18/362,418
- Filed: 2023-07-31
- Published: 2025-02-06
- Granted: 2025-02-04
- Earliest claimed: 2020-05-07
- Assignees: Bye Uas LLC
- Applicants: Bye UAS, Inc.
- Inventors: Grant Bishop; Valerie Saur; Shihlin Lu; Michael Fuller; Juan Naputi; Christopher Davis Smith; Kevin Sasso; April Kim; Daniel Serna
- Web page: https://patentgrep.com/patent/US12215466B1

## CPC

- G06Q 10/20: Administration of product repair or maintenance
- E01C 23/00: Auxiliary devices or arrangements for constructing, repairing, reconditioning, or taking-up road or like surfaces (apparatus for reconditioning of salvaged non-hydraulic compositions {for- in plant recycling of salvaged bituminous mixtures} E01C19/10)
- G06F 18/2431: Multiple classes
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06T 7/0004: {Industrial image inspection}
- G06T 7/001: {using an image reference approach}
- G06T 11/65: on geographic maps
- G06V 10/75: Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V 20/17: taken from planes or by drones
- G06V 20/182: {Network patterns, e.g. roads or rivers}
- B64U 2101/26: for manufacturing, inspections or repairs
- B64U 2101/32: for cartography or topography
- G06N 3/045: Combinations of networks
- G06T 2200/32: involving image mosaicing
- G06T 2207/10032: Satellite or aerial image; Remote sensing
- G06T 2207/10048: Infrared image
- G06T 2207/20021: Dividing image into blocks, subimages or windows
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30184: Infrastructure
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- E01C: CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR (forming roads or like surfaces by compacting or grading snow or ice E01H)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- B64U: UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR

## Abstract

An example embodiment of the present invention provides a method of assessing the condition of a pavement site, comprising: (a) acquiring aerial images of the site from above, for example by an unmanned aerial system (UAS); (b) using photogrammetry tools to generate an orthomosaic that represents the airport pavement surface; (c) using image analysis tools and machine learning methods to determine the location and extent of defects in the pavement; (c) producing an image representation of the site and the defects, where the location and extent of defects are discernible from the image; (d) using software application techniques to store and present defect data and other related information for client-side user access.

## Claims

### Claim 1

A method of determining and displaying pavement condition of airport pavement, comprising:
determining an orthomosaic of the airport pavement by scanning the airport pavement with an unmanned aerial system (UAS);
generating a plurality of subsections of the orthomosaic;
analyzing one or more subsections to determine a pavement condition index for that subsection, comprising using a neural network classifier to classify each subsection according to the presence of one or more of 16 types of pavement distresses set forth in ASTM D5340;
displaying to a user a representation of the pavement condition indices determined correlated with a representation of the airport pavement.

### Claim 2 (depends on 1)

The method of claim 1, wherein the step of determining an orthomosaic of the airport pavement comprises:
causing the UAS to overfly the airport pavement and acquire a plurality of images using a visible light camera, each image corresponding to a section of the airport pavement, where the sections overlap and together represent the entire portion of the airport pavement whose condition is to be determined;
using a computer to combine the plurality of images.

### Claim 3 (depends on 1)

The method of claim 1, wherein the orthomosaic is a georeferenced image of the airport pavement, where each pixel in the orthomosaic corresponds to a region of the airport pavement from 0.29 cm to 1.0 cm in size.

### Claim 4 (depends on 2)

The method of claim 2, wherein the step of causing the UAS to overfly the airport pavement further comprises causing the UAS to fly a mission comprising the following parameters: rectangular flight mode for the UAS; 75′ altitude; data capture at a speed of 11 mph; 70% front overlap; 75% side overlap; 90-degree gimbal pitch; dual grid for runway scans, single grid for taxiways and ramps; overlap of individual scans at least 10′; camera resolution 4000/3000, shutter 1/15, iso 200; images capture at least 5′ of turf on either side of pavement.

### Claim 5 (depends on 2)

The method of claim 2, wherein the step of causing the UAS to overly the airport pavement further comprises acquiring with the UAS a second plurality of images with one or more of the following: LIDAR, infrared imager, spectroscopic imager, radar, ground-penetrating sensor.

### Claim 6 (depends on 1)

The method of claim 1, wherein the step of displaying to a user a representation of the pavement condition indices further comprises generating a representation of the airport pavement combining a visual image of the airport pavement with representations of pavement distress at each corresponding location.

### Claim 7 (depends on 1)

The method of claim 1, wherein the step of displaying to a user a representation of the pavement condition indices further comprises determining a forecast pavement condition based on the determined pavement condition and a forecast maintenance schedule, and presenting the forecast pavement condition to the user.

### Claim 8 (depends on 1)

The method of claim 1, wherein the step of determining an orthomosaic further comprises acquiring a plurality of images of the airport pavement and applying photogrammetry to the plurality of images.

### Claim 9

An apparatus for determining and displaying pavement condition of airport pavement, comprising:
an unmanned aerial system (UAS), configured to overfly and collect images of the airport pavement;
an analysis system, configured to assemble subsections of the airport pavement images from the UAS, and to determine a pavement condition index for each subsection, comprising using a neural network classifier to classify each subsection according to the presence of one or more of 16 types of pavement distresses set forth in ASTM D5340;
a display system, configured to display to a user a representation of the airport pavement correlated with the determined pavement condition index for each subsection in the representation.

### Claim 10 (depends on 9)

The apparatus of claim 9, further comprising a prediction system configured to determine a predicted pavement condition from the determined pavement condition and a forecast maintenance schedule, and wherein the display system is further configured to display to the user the predicted pavement condition.

### Claim 11 (depends on 9)

The apparatus of claim 9, wherein the prediction system is further configured to determine a predicted pavement condition from the determined pavement condition and a forecast maintenance schedule and a forecast usage schedule.

### Claim 12 (depends on 9)

The apparatus of claim 9, wherein the analysis system is configured to use photogrammetry to produce an orthomosaic of the airport pavement by assembling subsections of the airport pavement images from the UAS.