Real-Time Car Detection and Driving Safety Alarm System With Google Tensorflow Object Detection API

Car accident is a serious social problem which often results in both life loss and financial loss. Most of car accidents are caused by a lack of safe distance between cars. To relieve this problem, in this paper we propose a real-time car detection and safety alarm system. The proposed system consists of two modules: real-time car detection module and safety alarm module. The proposed system is supposed to apply in a normal highway driving scenario. In the car detection module, the Google Tensorflow Object Detection (GTOD) API is employed. The function of GTOD API is to detect frontal cars in real-time and then mark them with rectangular boxes. As for the safety alarm module, it consists of three phases: to calculate the box width of detected cars; to calculate the safety factor; to determine the driving state. To justify the proposed system, a real highway experiment is conducted. The results show that the proposed system is able to appropriately indicate driving states: safe, dangerous and warning. By the given experimental results, it implies that the proposed system is feasible and applicable in the real-world applications.