Road Obstacle Detection

Road obstacle detection aims to automatically identify and classify objects obstructing roadways, crucial for autonomous driving and infrastructure maintenance. Current research heavily utilizes deep learning, particularly object detection models like YOLO and Faster R-CNN, focusing on improving accuracy and robustness across diverse conditions (e.g., varying lighting, object size, and unknown object types) and incorporating spatio-temporal context for enhanced performance. These advancements are vital for enhancing road safety, enabling autonomous vehicle navigation, and optimizing infrastructure management through efficient hazard identification and repair.