Deep Learning and Kinematic Telematics Integration for Real-Time Pothole Detection and Dynamic Navigation Routing

Abstract: Potholes represent a severe hazard to vehicular safety, traffic efficiency, and municipal road infrastructure sustainability. Traditional manual road inspection paradigms are inefficient, expensive, and structurally non-scalable. This paper presents a multimodal framework integrating vision-based edge deep neural networks and mobile kinematic sensor fusion for real-time pothole detection, localized classification, and dynamic navigation routing. The optical perception pipeline incorporates Contrast Stretching Adaptive Gaussian Star Filtering (CAGF) with POT-YOLOv8 and ResNet50-YOLOv8n architectures to isolate road anomalies under variable environmental conditions. Concurrently, dynamic vehicular impact is captured using three-axis accelerometer and gyroscope telemetry modeled via Quarter Vehicle Dynamics. Geotagged detection telemetry is transmitted via Application Programming Interfaces (APIs) to cloud routing servers, enabling dynamic path recalculations in Google Maps API platforms and generating automated notifications for public works departments. Empirical evaluations demonstrate benchmark object detection accuracies up to 𝟗𝟗. 𝟏𝟎 ± 𝟎. 𝟑𝟏% and real-time inference speeds of 18.4 ms/frame (≈ 𝟓𝟒. 𝟑 FPS) on edge hardware. Multimodal sensor fusion achieves a 77.5% reduction in false positives (lowering the false-positive rate to 3.2%) and field deployment accuracies between 𝟖𝟎% and 𝟖𝟕% (𝟖𝟑. 𝟓𝟎 ± 𝟑. 𝟐𝟎%), validating the framework for intelligent transportation systems.

Keywords: Pothole Detection, YOLOv8, Sensor Fusion, Dynamic Routing, Edge Computing, Geographic Information System