Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages

Abstract

Artificial intelligence models are currently being proposed for application in improving performance in addressing contemporary management and production issues. With the goal of automating the detection of road surface defects in transportation infrastructure management to make it more convenient, this research harnesses the advancements of the latest artificial intelligence models. Notably, new technology is used in this study to develop software that can automatically detect road surface damage, which shall lead to better results compared to previous models. This study evaluates and compares machine learning models using the same dataset for model training and performance assessment consisting of 9053 images from previous research. Furthermore, to demonstrate practicality and superior performance over previous image recognition models, mAP (mean average precision) and processing speed, which are recognized as a measure of effectiveness, are employed to assess the performance of the machine learning object recognition software models. The results of this research reveal the potential of the new technology, YOLO V5 (2023), as a high-performance model for object detection in technical transportation infrastructure images. Another significant outcome of the research is the development of an improved software named RTI-IMS, which can apply automation features and accurately detect road surface damages, thereby aiding more effective management and monitoring of sustainable road infrastructure.