Vehicle Speed Detection Using Depth Map on Real Time Systems

In the context of smart cities, real-time vehicle speed detection is a research area that can be examined under the categories of traditional and hybrid methods. Traditional methods encompass existing Radar and Lidar studies, while hybrid methods can be explored within the framework of artificial intelligence-supported systems. In this study, we propose a hybrid method that combines various vehicle speed detection techniques available in the literature. Our proposed method for determining road capacity and individual vehicle speeds in smart cities comprises three stages. In the first stage, we employ the YOLOv7 algorithm for vehicle detection, and in the second stage, we use the Centroid Tracker for tracking the detected vehicles. In the final stage, we calculate the displacement of each tracked vehicle within the frame through depth estimation, utilizing the Adabins algorithm. Additionally, we apply the ‘Triangle Method’ from the literature to find the displacement of each tracked vehicle one second after depth estimation. To evaluate our approach, we conducted sequential experiments using a data set of 6000 images, which were subdivided into training (85%), validation (10%), and testing (5%) sets. Our trained model recognizes two classes: ‘car’ and ‘truck,’ achieving precision rates exceeding 90% for each class.