Vehicle Detection with YOLOv7 on Study Case Public Transportation and General Classification, Prediction of Road Loads

The Intelligent Transportation System (ITS) is a part of the application of computer vision to transportation systems, which is nothing more than a form of integration between information systems, telecommunication and transportation infrastructure, vehicles, and road users. As a result, ITS can not only solve traffic problems, but also reduce the use of private vehicles and increase the efficiency of public transportation by the community if road users’ comfort and safety continues to improve. The implementation of ITS in several developed countries serves as a model for its achievements. In this study, YOLOv7 was used to classify vehicles using CCTV data from ATCS Bandung City. Taking a number of data to obtain enough data for further separation of data from the CCTV image capture into parts of the vehicle class. A pretraining model is used to identify the target vehicle based on this classification. This data processing allows for the prediction and calculation of road loads, which have long been a source of traffic congestion in Bandung, particularly in the Dago area.

I. Introduction

The development and deployment of Intelligent Transportation System (ITSs) provide better accuracy for Traffic flow prediction. It is deal with as a crucial element for the success of advanced traffic management systems, advanced public transportation systems, and travelers information systems [1].