Speed Bump Detection Model for Advanced Driver Assistance System

Since the invention of the automobiles, irrespective of whether the internal combustion (IC) engines or electric vehicles, driving them on the road without causing any accidents has been a challenge for many years for the mankind. For the autonomous vehicles, identifying the obstacles and manoeuvring the vehicle in the traffic is another challenge. To reduce the speed of the vehicles wherever essential, the speed bumps are provided. This paper discusses the detection of speed bump using YOLOv8 object detection framework as a part of Advanced Driver Assistance System (ADAS). YOLOv8 object detection framework is used in the development of the model. The trained model has achieved 77.03% precision, 74.92% recall and F 1 -score is 75.9609. The trained model is deployed in Raspberry Pi 4 Model B and speed bump detection is verified.