Traffic Object Detection

Traffic object detection aims to accurately and efficiently identify vehicles, pedestrians, and other relevant objects within traffic scenes, primarily for applications in autonomous driving and intelligent transportation systems. Current research emphasizes improving detection accuracy and speed, particularly in challenging conditions like low light or inclement weather, often employing advanced architectures like transformer-based networks and multi-task learning approaches that integrate object detection with other perception tasks such as lane detection and drivable area segmentation. These advancements are crucial for enhancing the safety and reliability of autonomous vehicles and optimizing traffic management systems.