Road Damage Detection Using YOLO with Smartphone Images
Dongjun Jeong
Abstract
Deep learning-based technology is a good key to unlock the object detection tasks in our real world. By using deep neural networks, we could break a problem that is dangerous and very time-consuming but has to be done every day like detecting the road state. This paper describes the solution using YOLO to detect the various types of road damage in the IEEE BigData Cup Challenge 2020. Our YOLOv5x based-solution is light-weight and fast, even it has good accuracy. We achieved an F1 score of 0.58 using our ensemble model with TTA, and it could be an adequate candidate for detecting real road damage in real-time.
Topics & Concepts
Deep learningComputer scienceObject detectionArtificial intelligenceKey (lock)Computer visionBig dataObject (grammar)Artificial neural networkReal-time computingPattern recognition (psychology)Computer securityData miningInfrastructure Maintenance and MonitoringAdvanced Neural Network ApplicationsVehicle License Plate Recognition