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This paper introduces an end-to-end, vision-based framework for autonomous pavement inspection, computing the Pavement Condition Index (PCI) directly from 2D imagery. The system employs deep learning-based object detection and ensemble segmentation to identify road distress types, including surface weathering, and uses a monocular diffusion model for depth estimation. Validated on smartphone-captured footage, the pipeline demonstrates reliable performance in detection, measurement, and PCI scoring, suggesting its suitability for deployment on drones or ground robots.
A deep learning pipeline can reliably compute Pavement Condition Index (PCI) from smartphone videos, opening the door to low-cost, automated road inspection via drones or robots.
Advancements in robotics and computer vision are transforming how infrastructure is monitored and maintained. This paper presents a novel, fully automated pipeline for pavement condition assessment that integrates real-time image analysis with PCI (Pavement Condition Index) computation, which is specifically designed for deployment on mobile and robotic platforms. Unlike traditional methods that rely on costly equipment or manual input, the proposed system uses deep learning-based object detection and ensemble segmentation to identify and measure multiple types of road distress directly from 2D imagery, including surface weathering, a key precursor to pothole formation often overlooked in previous studies. Depth estimation is achieved using a monocular diffusion model, enabling volumetric assessment without specialized sensors. Validated on real-world footage captured by a smartphone, the pipeline demonstrated reliable performance across detection, measurement, and scoring stages. Its potential hardware-agnostic design and modular architecture position it as a practical solution for autonomous inspection by drones or ground robots in future smart infrastructure systems.
