An Android Application for Unwanted Vehicle Detection and Counting

The significance of vehicle identification and monitoring is increasing in the field of traffic management. The Intelligent Transportation System (ITS) is a highly efficient way to address the issue of traffic congestion in metropolitan areas and is a prominent focus in the development of smart cities. One specific application is the monitoring and forecasting of fluid flow. The suggested system aims to implement automated vehicle identification and recognition processing utilizing static image datasets. Significant progress in vehicle detection technology has been made due to the emergence of unmanned driving and intelligent transportation research. The suggested system utilizes the deep learning technique to investigate the vehicle detection algorithm, specifically employing the fundamental phase target detection algorithm known as the YOLO algorithm. Hence, the initial approach involves the manipulation of visual data from a publicly available collection of road vehicles for the purpose of training. A vehicle detection model is developed using the YOLO algorithm to demonstrate the detection performance separately. The suggested system's contribution is in the enhancement of the conventional YOLO network's architecture, enabling precise identification of vehicle targets.