jborau/DLR_v1_detection

DLR v1 — 2D detection + instance segmentation

Multi-view roadside + drone traffic imagery from three German sites, with 2D boxes, per-instance masks and class labels in COCO format.

Each position is a time-synchronized pair: a drone bird's-eye view (3840×2160) and one or more fixed Axis roadside cameras (1920×960) watching the same intersection.

Contents

train/  _annotations.coco.json  +  <run_key>/*.jpg   (34 subdirs, 18015 images)
valid/  _annotations.coco.json  +  *.jpg             (5892 images)
test/   _annotations.coco.json  +  *.jpg             (5880 images)

split	images	instances	source runs
train	18,015	375,926	34
valid	5,892	104,024	24
test	5,880	105,952	24
total	29,787	585,902	58

train/ images are grouped into one subdirectory per source run because the Hub caps a directory at 10,000 files. This is transparent to loaders: COCO file_name is a relative path, so os.path.join(split_dir, file_name) resolves correctly in all three splits.

Classes

id	name	instances
0	person	0
1	bicycle	0
2	car	528,354
3	motorcycle	16,048
4	bus	3,202
5	truck	12,549
6	van	25,749

person and bicycle are kept for COCO id compatibility but carry no annotations — the prompt set targets vehicles only. van is id 6 so the standard COCO ids 0–5 stay stable.

Traffic is overwhelmingly cars (90%); bus is rare (0.5%). This is a long-tailed vehicle dataset, not a balanced one.

Format

Standard COCO. Detection and instance segmentation share one file — masks are compressed RLE in segmentation.

{
  "id": 1, "image_id": 0, "category_id": 2,
  "bbox": [x, y, w, h],          // xywh, pixels
  "area": 3896,                   // == mask pixel count, not bbox area
  "iscrowd": 0,
  "segmentation": {"size": [h, w], "counts": "..."},   // full-frame RLE
  "score": 0.96,                  // detection confidence
  "track_id": 1474,               // stable within its source run
  "global_id": "sb_pos3_drone_0001_1474"
}

Loads with pycocotools; RLE round-trips (area == mask.sum() verified on every split).

global_id is clip-scoped ("<run>_<track_id>") — it identifies a vehicle within one camera, not across cameras.

Image file names are <run_key>_<source_frame_index>.jpg, so every image is traceable to its source video and frame.

import os
from pycocotools.coco import COCO

split = "train"
coco = COCO(f"{split}/_annotations.coco.json")
img = coco.loadImgs(coco.getImgIds()[0])[0]
path = os.path.join(split, img["file_name"])          # works for every split
anns = coco.loadAnns(coco.getAnnIds(imgIds=img["id"]))
mask = coco.annToMask(anns[0])                        # HxW binary

Splits

Split by position, then a frame-level cut — not a random shuffle. Randomly splitting video frames leaks near-identical neighbours across splits and inflates scores.

• train positions are exclusive: their frames never appear in valid/test.
• eval positions feed both valid and test, cut temporally (first part → valid, remainder → test). valid and test may share a position; neither shares one with train.

Frames are selected with a gap + min-vehicle rule rather than a fixed stride: keep a frame only if it has ≥3 annotations and is ≥25 source frames (~1 s) after the last kept one, which drops near-duplicate consecutive frames.

Verified: 0 source-run overlap between train and eval.

Caveat — Tostmannplatz

At Tostmannplatz the split is per camera: tp_pos1's drone and cam_3_100 are in eval while its cam_48_101/cam_48_106 are in train. Those cameras observe the same intersection at the same instant from different viewpoints, so the same traffic appears on both sides of the split, seen from different angles. Exclude the tp_pos1_* runs from train if you need a strictly scene-disjoint evaluation.

Notes

• Strongly class-imbalanced (90% car).
• person/bicycle are empty by construction.
• Frames within a split are temporally correlated (~1 s apart within a run).
• Three sites only; limited weather and lighting diversity.

Contact: Javi Borau — jborau@caos.uc3m.es