Road Condition Assessment Using Handheld Image Capture

The patent replaces manual physical measurements with an automated image processing system. The system captures images of pavement distresses using cameras mounted on vehicles or handheld devices, then uses computer vision algorithms to automatically measure geometric properties such as length, width, and area of distresses. This substitution of mechanical measurement with optical and computational methods dramatically reduces inspection time while maintaining measurement precision.

The patent creates digital copies of pavement conditions through image capture. Instead of physically measuring each distresse, the system captures visual representations (images) of the pavement surface and distresses, then processes these copies to extract quantitative data. This copying approach allows for rapid, non-contact measurement of multiple distresses simultaneously across large areas.

The patent implements feedback mechanisms to correct for lighting conditions. The system captures multiple images at different times and angles, then uses algorithms to compare and validate measurements. When extraneous light sources are detected, the system can request re-capture or apply correction factors to maintain measurement precision while preserving the speed benefits of image processing.

The patent performs preliminary actions to prepare for accurate measurement. Before capturing images, the system pre-processes the data by identifying and marking potential distress locations, then captures additional images specifically targeted at those areas. This preliminary identification allows the system to focus measurement resources on critical areas while minimizing the impact of extraneous lighting conditions.

The patent enables the system to perform self-service measurement. Instead of relying on human observers to subjectively assess and measure distresses, the automated image processing system independently identifies, measures, and quantifies pavement conditions. The system self-calibrates using reference objects and automatically applies standardized measurement protocols, eliminating inter-observer variability while maintaining operational simplicity through automated data collection.

The patent uses color and visual pattern recognition to objectively identify and characterize distresses. The system analyzes color changes, patterns, and visual characteristics of pavement distresses to determine their type, severity, and location. This objective visual analysis replaces subjective human assessment, providing consistent and reproducible measurements while maintaining ease of operation through automated image analysis.

The patent employs a universal multi-functional inspection system. A single integrated platform combines image capture, GPS positioning, data processing, and distress identification capabilities in one system. The same hardware and software infrastructure used for capturing images also performs measurement, mapping, and analysis functions, eliminating the need for multiple specialized devices while maintaining comprehensive data collection reliability.

The patent merges multiple inspection functions into a unified system. Instead of using separate devices for image capture, measurement, mapping, and data analysis, the system integrates these functions into a single coordinated platform. The handheld device or vehicle-mounted system simultaneously captures images, records location data, identifies distresses, and processes measurements, reducing overall system complexity while maintaining comprehensive inspection capability.