Detecting road potholes using computer vision techniques
Neil Camilleri, Thomas Gatt
Abstract
The number of road potholes is growing day by day due to the increase in vehicles. This is also increasing the number of vehicle accidents caused by lack of road maintenance. In this study, a road pothole detection system using computer vision techniques was conducted. Object detection algorithms such as the YOLO, and SSD were selected. The YOLOv3-SPP model obtained the best mAP of 68.83%, while the YOLOv3-tiny inferred an image in just 0.01s. Testing was also done on an Android device and a Raspberry Pi in order to evaluate performance on embedded systems. The SSDLiteMobileNet v2 converted to a TensorFlow Lite model outperformed all the YOLOv3 models when time is compared. In this paper we show that the model with the best performance is YOLOv3 SPP, but if time is a priority, the SSD TFLite model is the right fit.