One example of this is lane detection, which helps autonomous vehicles identify different lanes and boundaries on the road. By annotating lines in images, we can train AI algorithms to recognize lanes and navigate smoothly in traffic without any mishaps. This also helps vehicles to distinguish roads from other surfaces and stay on track.
Another important aspect of autonomous driving is object tracking, which involves locating objects and tracking them in multiple frames or sequences. By annotating objects with bounding boxes, we can train AI algorithms to detect other cars and track their movements. With machine learning, autonomous vehicles can identify other cars and objects in the real world and navigate in a way that mimics human behavior.
Image segmentation is another important aspect of autonomous driving, as it helps vehicles understand their surrounding environment. By dividing images into sets of pixels that correspond to real-world categories using polygon annotation, AI models can learn to experience and understand variables they might encounter in the real world, such as lighting, angles, and obstacles. This helps autonomous vehicles make accurate decisions in complex environments.
Finally, irregular shape detection is important for autonomous vehicles to recognize irregularly shaped objects on the road, such as pedestrians or traffic signs. With the help of polygon annotation, computer vision for autonomous vehicles can be trained to recognize these irregularly shaped objects and differentiate them from other elements on the road.