Deep Learning Approaches for Pedestrian and Cyclist Detection in Mixed Traffic Environments

One of the biggest challenges in contemporary metropolitan areas is making sure that bikes and pedestrians are safe in mixed-traffic situations. In a variety of changing traffic situations where non-motorized road users are at a significant danger, traditional detection technologies frequently exhibit poor accuracy. For precise pedestrian and bicycle monitoring and detection in mixed traffic situations, this study suggests a strong deep learning-based method. Our model successfully differentiates between bikes, pedestrians, and other kinds of vehicles by utilizing a convolutional neural network (CNN) architecture combined with region proposal networks (RPNs). Furthermore, to improve the model’s resilience to occlusions and changing lighting, we use data augmentation approaches. Finally, we use transfer learning to adjust our model to various urban environments. Our approach outperforms conventional detection systems in terms of precision and recall rates, as demonstrated by extensive testing on real-world datasets. By increasing situational awareness and boosting safety for vulnerable road users, our method promises to make a substantial contribution to autonomous vehicles and traffic control systems.