dastrix/polylane-roadway-yolo11l-seg

polylane-roadway-yolo11l-seg

YOLO11-l-seg fine-tuned for roadway (asphalt road surface) segmentation in aerial drone footage of urban / residential streets.

Training

• —Base model: yolo11l-seg.pt (Ultralytics, COCO-pretrained)
• —Data: ~2 973 frames (2 646 train / 327 val) automatically annotated with SAM 3 text-prompted segmentation across 6 drone videos (~50 K frames total), Kashkadarya region (Uzbekistan)
• —Split: train = 5 videos, val = 1 video (no overlap)
• —Image size: 1280
• —Batch size: 16 × 3 GPUs (NVIDIA B200)
• —Epochs: 50, AdamW, cos LR, lr0=0.001, mixup=0.1, copy_paste=0.1
• —Wall time: ~28 min on 3× B200

Metrics (val on held-out video)

The roadway is a "stuff" class (one large region per image), so instance mAP under-reports semantic-segmentation quality. Visual mask IoU is closer to 0.70–0.80 on most frames.

Limitations

• —Trained on residential / village aerial drone footage only — generalisation to highway/dashcam not validated.
• —Training labels come from SAM 3 auto-annotation, which fails (~5–15 %) on far frames where grey rooftops are confused with asphalt. Some noise inherited.

Usage

from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict("frame.jpg", imgsz=1280, conf=0.25)
results[0].show()

License

Apache 2.0