Deep Learning Approaches

Voice-over: ../assets/audio/deeplearning.mp3

Deep learning improved crosswalk detection by learning features directly from data instead of hand-crafted rules. Two common strategies are: object detection (e.g., Faster R-CNN, YOLO) to localize crosswalk regions and semantic segmentation (e.g., U-Net, DeepLab) to label pixels that belong to the crosswalk pattern. These models generally handle wear, mild occlusion, and viewpoint changes better than classical methods [2] [7].

Performance still depends on training diversity. Night scenes, heavy shadows, or new cities can cause drops due to domain shift. For deployment, papers report practical metrics such as FPS/latency and power, and explore compression/distillation for real-time use on devices like Jetson [7].