Target-PLP

Target-PLP is a cross-domain test-only dataset for zero-shot evaluation of aerial power-line detection models.

It contains 194 aerial frames annotated with polyline instance labels in YOLinO format. Models are trained on TTPLA and evaluated on Target-PLP without fine-tuning.

Compared to TTPLA, Target-PLP is more complex and challenging: denser wire layouts, more parallel instances per frame, greater scene variation, and a different imaging domain. Annotations are ordered polylines (vertex sequences along each wire), not axis-aligned boxes or masks.

Statistics

Split	Images	Resolution	Purpose
test	194	512×512	zero-shot evaluation only

No train or validation split is provided.

Preprocessing

1. Source: CVAT XML exports from aerial video frames
2. Resize: each frame is stretch-resized to 512×512 (LANCZOS); polyline vertices scaled with sx = 512/W, sy = 512/H
3. No tiling / no dual-crop — one full frame per image
4. Frames without valid polyline instances (fewer than 2 points) are skipped

Directory layout

images/test/*.png
labels/test/*.npy

Image and label files share the same stem, e.g. ANAR4318_frame_000000.png ↔ ANAR4318_frame_000000.npy.

Annotation format (polyline)

Each labels/test/<stem>.npy is a Python dict (format version 3):

{
    "polylines": [                    # one polyline per power-line instance
        [[x1, y1], [x2, y2], ...],   # instance 1: ordered vertices (pixels)
        [[x1, y1], [x2, y2], ...],   # instance 2
        ...
    ],
    "instance_ids": [1, 2, ...],      # unique ID per wire instance
    "source_file": "<stem>",
    "image_size_wh": [512, 512],
    "format_version": 3
}

• Coordinates are in pixel space after the 512×512 resize
• Each polyline is an ordered sequence of 2D points along one wire
• Multiple parallel wires are separate instances with distinct instance_ids

Loading example

import numpy as np
from PIL import Image

root = "./Target-PLP"
stem = "ANAR4318_frame_000000"

img = Image.open(f"{root}/images/test/{stem}.png")          # 512×512 RGB
label = np.load(f"{root}/labels/test/{stem}.npy", allow_pickle=True).item()

polylines    = label["polylines"]       # list[list[[x, y]]]
instance_ids = label["instance_ids"]    # list[int]

Download

hf download V8heart/Target-PLP \
  --repo-type dataset \
  --local-dir ./Target-PLP

Related

• TTPLA training benchmark: V8heart/yolino-ttpla-benchmark
• Code: V8heart/CAPSTONE