Real-time traffic object detection using detectron 2 with faster R-CNN

Object detection is becoming more and more important in daily life, especially in applications like advanced traffic analysis, intelligent driver assistance systems, and driverless cars. The accurate identification of objects from real-time video is crucial for effective traffic analysis. These systems play a vital role in providing drivers and authorities a comprehensive understanding of the road and surrounding environment. Modern algorithms and neural network-based architecture with extremely high detection accuracy, like Faster R-CNN are crucial to achieving this. This study investigates an advanced object detection system designed for urban traffic applications using an interactive Gradio interface and Detectron2’s Faster R-CNN model. The research focuses on developing a model capable of identifying key traffic objects such as traffic lights, vehicles, buses, crossroads etc., with high accuracy and precision. A significant contribution of this study is the integration of Gradio-based interface that enables users to upload images or videos from their local storage or webcam and view the results in real time making the model both accessible and practical. Our findings demonstrate that the Detectron2 framework, paired with Gradio’s interactive interface offers a reliable and scalable solution for traffic monitoring and safety applications.