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.
