Design and Application of UAV-Based Intelligent Inspection Technology for Highways

Manual highway inspection in conventional practice suffers from low operational efficiency, elevated safety risks for field personnel, and unavoidable inspection blind spots across key infrastructure sections. To address these prominent challenges, this research develops an integrated intelligent UAV inspection system that synergizes flight control technology, Beidou positioning, machine vision, and artificial intelligence algorithms. The system enables rapid detection and accurate identification of structural defects on highway pavements, bridges, slopes, and other critical infrastructure components. We conduct targeted optimizations on both the YOLOv7-RDD object detection algorithm and the DeepLabV3+ segmentation algorithm, optimizations that markedly boost the accuracy and environmental robustness of defect recognition in complex highway scenarios. A dedicated deployment scheme featuring automatic UAV airports and intelligent route planning is also proposed to underpin fully automated, high-frequency inspection operations. Field engineering applications have validated the system’s practical effectiveness: defect recognition accuracy surpasses 95% in real-world highway environments, while overall inspection efficiency is improved by approximately 80%. This research delivers pivotal technical support for the intelligent maintenance and refined management of transportation infrastructure networks.