SAE Eletroquad 2026 - Hook segmentation (YOLO26n-seg)

Instance-segmentation model for the "hang the wire" hook mission of the SAE Brasil Eletroquad 2026 competition, trained by Black Bee Drones. It segments two classes - rose (each visible segment of the suspended rope) and sphere (the orange sphere on the rope) - and runs on every control-loop tick of the mission. The team finished 2nd overall (official results).

• Base model: yolo26n-seg.pt (Ultralytics)
• Task: instance segmentation
• Classes: rose (0), sphere (1)
• Input size: 960
• Dataset: blackbeedrones/sae-2026-hook

Mission

The drone takes off, finds the orange sphere mounted on one of two suspended ropes, parks a fixed distance from it, picks which side of the rope to fly along, turns perpendicular to the rope, descends on LIDAR, releases a hook with a servo, and lands. Because each visible rope segment is its own rose instance, the controller can measure both rope arms and choose a side. The mission runs on a Jetson Orin Nano through Nectar SDK.

Results

Evaluated on the dataset's test split (395 images, 501 instances), NMS iou=0.6. The numbers below are Ultralytics-native (COCO 101-point interpolation); see SUMMARY.md for the full breakdown, including the SDK/torchmetrics figures (which run a different curve discretization and read systematically lower at mAP@50-95).

Per-class mAP@50-95:

Operating-point precision / recall / F1 at the per-class optimal confidences (from the F1 curve):

Recommended inference settings

Predict at the lower of the two confidences and apply a per-class filter, so each class keeps its own threshold.

Usage

Nectar SDK

from nectar.ai.segmentation import Segmentor
from nectar.ai.detection.postprocess import PerClassConfidenceFilter

# loads weights/best.pt from the Hub
segmentor = Segmentor("blackbeedrones/sae-2026-hang-all-yolo26n-seg-v2-960")
segmentor.load()

result = segmentor.segment(image, conf=0.47, iou=0.6, imgsz=960)

# rose=0, sphere=1
per_class = PerClassConfidenceFilter(threshold_mapping={0: 0.47, 1: 0.70}, default_threshold=0.47)
kept = per_class.filter(result.to_supervision())
for seg in result:
    print(seg.class_name, f"{seg.confidence:.2f}", f"area={seg.mask_area}px")

Ultralytics

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download("blackbeedrones/sae-2026-hang-all-yolo26n-seg-v2-960", "weights/best.pt")
model = YOLO(weights)
results = model.predict("image.jpg", imgsz=960, iou=0.6, conf=0.47)

Training

Trained with Ultralytics through the Nectar SDK on blackbeedrones/sae-2026-hook (Roboflow sae-2026-hang v2: 9,235 train / 396 val / 395 test images). Full configuration in experiment.config.yaml.

Files

• weights/best.pt - recommended checkpoint (best validation).
• weights/last.pt, weights/epoch*.pt - last and per-epoch checkpoints.
• evaluation/ - curves, confusion matrix, prediction samples, per-class CSV/JSON.
• experiment.config.yaml, args.yaml, results.csv - training configuration and log.
• SUMMARY.md - full evaluation breakdown and threshold analysis.

References

• Dataset: blackbeedrones/sae-2026-hook
• Nectar SDK
• SAE Brasil Eletroquad
• Eletroquad 2026 official results