An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles

TL;DR

This paper presents an improved CNN model for autonomous road inspection using UAVs, capable of detecting road cracks, potholes, and yellow lanes to enable navigation and reporting in real-time.

Contribution

An enhanced CNN architecture is developed and implemented for UAV-based road inspection, improving detection accuracy and efficiency over previous models.

Findings

Improved model shows higher accuracy and mAP in detection tasks.

Real-time implementation on UAV demonstrates practical autonomous inspection.

Benchmarking confirms superior performance of the proposed model.

Abstract

Advancements in artificial intelligence (AI) gives a great opportunity to develop an autonomous devices. The contribution of this work is an improved convolutional neural network (CNN) model and its implementation for the detection of road cracks, potholes, and yellow lane in the road. The purpose of yellow lane detection and tracking is to realize autonomous navigation of unmanned aerial vehicle (UAV) by following yellow lane while detecting and reporting the road cracks and potholes to the server through WIFI or 5G medium. The fabrication of own data set is a hectic and time-consuming task. The data set is created, labeled and trained using default and an improved model. The performance of both these models is benchmarked with respect to accuracy, mean average precision (mAP) and detection time. In the testing phase, it was observed that the performance of the improved model is better in respect of accuracy and mAP. The improved model is implemented in UAV using the robot operating system for the autonomous detection of potholes and cracks in roads via UAV front camera vision in real-time.