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 PoribohonBD with vehicle images.
Vehicle detection is a critical step in the development of intelligent transportation systems (ITS).
The challenges of vehicle detection on urban roads arise from the camera position, context
variations, obstruction, multiple current frame objects, and transportation pose. The current study
provides a synopsis of state-of-the-art vehicle detection techniques, which are classified thus
according to motion and aesthetics techniques, beginning with frame differencing and background
subtraction and progressing to feature extraction, a more complicated model in comparative
analysis. The pre-processed data, as well as the fine-tuning hyper parameter, then input into the
cutting-edge YOLOv5s deep learning model for autonomous vehicle detection and recognition. In
the end, the training accuracy averaged 0.79% to detect vehicles in all classes.
