Paper Title
Road Crack Detection for Automated Infrastructure Maintenance

Abstract
This paper suggests a revolutionary and path-breaking solution for automatic identification of road damage through the use of state-of-the-art deep learning models with the integration of Unmanned Aerial Vehicles (UAVs). Road maintenance is the highest priority since it is crucial in ensuring travel experiences are efficient, safe, and smooth for all users. But conventional manual road surveys are highly time-consuming, costly, and risky for those involved in the evaluation. In an attempt to effectively counter and counteract such issues, we introduce a new UAV-based solution based on state-of-the-art intelligent algorithms particularly used for identifying and pinpointing road damage accurately. Our solution utilizes the object detection capability of YOLOv4, YOLOv5, and YOLOv7 models to thoroughly scan through UAV images. We trained and performed many tests using testing techniques for our research using two important datasets: China's RDD2022 dataset and Spain's road dataset. Experimental results that we achieved validate our suggested solution as truly effective, with the average of (mAP@.5) of 59.9% using the YOLOv5 model, an impressive 65.70% using a variant of the YOLOv5 model with a Transformer Prediction Head, and an impressive 73.20% using the new YOLOv7 model. Such results prove the significant advantages and benefits that are acquired by the utilization of UAVs with the technology for automatic road damage identification, providing an affordable system with tremendous improvements to monitoring and maintaining key infrastructure. Keywords - Road Crack detection, YOLOv 7, Convolutional Neural Networks, Deep Learning, RDD2022, Transportation maintenance