Pedestrian detection constitutes a fundamental component of Advanced Driver Assistance Systems (ADAS). playing a pivotal role in ensuring road safety and reducing the risk of accidents. With the emergence of deep learning methodologies, significant progress has been made in the field of pedestrian detection, leading to the development of state-of-the-art detectors that exhibit impressive accuracy and efficiency in real-world scenarios. However, despite these advancements, there are ongoing challenges that need to be addressed to further enhance the performance and robustness of pedestrian detection systems, particularly in adverse environmental conditions such as varying lighting conditions, and adverse weather. Additionally, there is a growing demand for pedestrian detection systems to incorporate distance estimation capabilities, with an extension to include cyclists and riders, who are also crucial for ensuring overall road safety. To address these challenges, our research focuses on creating a stereovision system using a Raspberry Pi 4, designed to detect pedestrians, cyclists, and riders, and to estimate their 3D distances in real-time. The initial phase of our study involves enhancing the performance of the YOLOv5s through a fine-tuning process using a custom dataset and leveraging advanced augmentation techniques. Through rigorous evaluation and comparison with the original YOLOv5s model, we demonstrate a significant improvement in detection accuracy, exceeding 79%. The second phase of our study focuses on real-time depth estimation using stereo camera calibration techniques and triangulation methods. Through triangulation techniques involving object detection results, the actual distance estimation algorithm is validated in real-time for a single person. Thus, our research presents a comprehensive approach to person detection and distance estimation, leveraging the synergies between deep learning and stereovision technology.
Rachidi et al. (Mon,) studied this question.