Ensuring precise perception of the surrounding road environment is crucial for the safe functioning of autonomous vehicles in the domain of autonomous driving. Using cutting-edge deep learning techniques, this research presents a novel way for autonomous road environment classification and item detection. It focuses on combining Yolov5 and multiscale small object detection models. Modern object detection frameworks allow for the accurate and efficient processing of a wide range of things that are met on the road, such as cars, bikes, pedestrians, and traffic signals. By means of the smooth integration of these models, the proposed system exhibits resilience and efficiency in various real-life situations, indicating noteworthy progressions in the field of autonomous driving technology. The efficacy and dependability of the proposed strategy have been confirmed by extensive testing and assessment. The system delivers significant gains in efficiency and accuracy of detection by incorporating the deep learning models, providing a solid basis for the creation of safer and more reliable autonomous cars. This study opens the door to a future in which self-driving cars navigate roadways with increased safety and efficiency by demonstrating the critical role that cutting-edge deep learning algorithms play in enabling precise perception and decision-making capabilities within autonomous driving systems.
Jeny et al. (Wed,) studied this question.