Automated Dhaka City Vehicle Detection for Traffic Flow Analysis Using Deep learning

Abstract

There are many ways to stop traffic jams from spreading, and one of the most effective is to detect the vehicle. The uniqueness of Dhaka's traffic situation creates a complicated and difficult occurrence, with over eight million passengers passing through the city every day in a 306 square kilometer area. To address this issue, our research includes a deep learning methodology for autonomous vehicle detection and localization from optical scans. Data preparation was done using annotated data from Poribohon-BD with vehicle images. Vehicle detection is a vital stage in the development of autonomous vehicles (ITS). The camera position, context fluctuations, obstacle, multiple current frame objects, and transportation stance all contribute to the difficulty of vehicle detection on urban highways. The current study provides a synopsis of state-of-the-art vehicle identification techniques, which are classified thus according to motion and aesthetics techniques, beginning with frame differencing and background subtraction and continuing to feature extraction, a more complicated model in comparative analysis. The pre-processed data, as well as the fine-tuning hyperparameter, are then fed further into cutting-edge YOLOv5s deep learning algorithm.