Convergence of Stereo Vision-Based Multimodal YOLOs for FasterDetection of Potholes

Road potholes can cause serious social issues,such as unexpected damages to vehicles and traffic accidents.For efficient road management,technologies that quickly find potholes are required,and thus researches on such technologies have been conducted actively.The three-dimensional(3D)reconstruction method has relatively high accuracy and can be used in practice but it has limited application owing to its long data processing time and high sensor maintenance cost.The two-dimensional(2D)vision method has the advantage of inexpensive and easy application of sensor.Recently,although the 2D vision method using the convolutional neural network(CNN)has shown improved pothole detection performance and adaptability,large amount of data is required to sufficiently train the CNN.Therefore,we propose a method to improve the learning performance of CNN-based object detection model by artificially generating synthetic data similar to a pothole and enhancing the learning data.Additionally,to make the defective areas appear more contrasting,the transformed disparity map(TDM)was calculated using stereo-vision cameras,and the detection performance of the model was further improved through the late fusion with RGB(Red,Green,Blue)images.Consequently,through the convergence of multimodal You Only Look Once(YOLO)frameworks trained by RGB images and TDMs respectively,the detection performance was enhanced by 10.7%compared with that when using only RGB.Further,the superiority of the proposed method was confirmed by showing that the data processing speed was two times faster than the existing 3D reconstruction method.