---
license: other
license_name: see-license-file-upstream-unstated
license_link: LICENSE
task_categories:
- object-detection
language:
- en
- zh
tags:
- power-line
- insulator
- insulator-defect
- uav
- aerial-imagery
- electrical-inspection
- cpld
- object-detection
- pascal-voc
- yolo
- keenforge
size_categories:
- 1K<n<10K
---

# CPLID_Insulators-corrected

[![DOI](https://img.shields.io/badge/DOI-10.57967%2Fhf%2F10878-blue)](https://doi.org/10.57967/hf/10878)

**Power-line insulator defect detection — cleaned, split and standardized version of the Chinese Power Line Insulator Dataset (CPLID).**
**电力巡线绝缘子缺陷检测 —— 中国电力线绝缘子数据集（CPLID）的清理、划分与规整版。**

[English](#english) · [中文](#中文)

---

## English

### What is this?

A **cleaned, split and standardized object-detection version** of the
**Chinese Power Line Insulator Dataset (CPLID)** — normal insulators captured by
UAVs plus synthetically defective insulators. Images were provided by the
**State Grid Corporation of China**; the dataset was assembled by **WANG Zi-Hao**.

Reference: Tao, Zhang, Wang, Liu, Zhang & Xu — *"Detection of Power Line Insulator
Defects Using Aerial Images Analyzed With Convolutional Neural Networks"*,
**IEEE Transactions on Systems, Man, and Cybernetics: Systems**, 2018.
Upstream repository: [InsulatorData/InsulatorDataSet](https://github.com/InsulatorData/InsulatorDataSet).

The original release ships as two flat, unsplit folders (`Normal_Insulators` and
`Defective_Insulators`), where each defective image is annotated **twice** (once
for the defect, once for the whole insulator) in two parallel label folders, the
VOC metadata is polluted with hard-coded personal paths, a stray non-image file
sits in the archive, and 3 images are **pixel-identical duplicates**.

This release removes the duplicates, keeps consecutive UAV-capture frames on one
side of the split, merges the paired label folders into a single annotation per
image, cleans the VOC metadata, drops the stray file, provides a reproducible
**train/val/test** split, and ships ready-to-train **YOLO TXT** + **Pascal VOC XML**
annotations with configuration files.

> ⚠️ **Upstream license: none stated.** CPLID ships no LICENSE file and no terms of use.
> See [License & attribution](#license--attribution) — verify before commercial use.

### Corrections vs. the official release

| # | Correction | Detail |
|---|---|---|
| 1 | **Removed 3 duplicate images** | Three pairs in `Normal_Insulators` are **pixel-identical** (`0150` = `2984`, `0852` = `1876`, `1608` = `1938`). The higher-numbered copy of each pair was removed, so identical content can never straddle the new split. |
| 2 | **Consecutive frames kept together** | The 600 normal images are UAV video frames; 112 of them sit next to a numerically consecutive neighbour. Frames are grouped into **486 capture chains** (longest: 5 frames) and each chain is assigned **entirely** to one split, so no near-identical burst is split across train/val/test. |
| 3 | **Merged the paired label folders** | Upstream annotates every defective image twice in parallel folders (`labels/defect/` + `labels/insulator/`). Here the two are merged into **one XML / one YOLO file per image**, containing the insulator box and the defect box together. |
| 4 | **Reproducible train/val/test split** | Upstream ships **no split at all**. This release provides a seeded, class-stratified **70 / 15 / 15** split (seed 42) over the frame-chain groups. |
| 5 | **VOC metadata clean-up** | The upstream XML carried a bogus `<folder>` value (a mix of `images`, `long` and empty) and a hard-coded local `<path>` exposing personal absolute paths with Chinese characters (e.g. `/media/laptop/vbox/硕士毕业设计/小论文/DataSet/...`). Both are normalised, and `<source><database>` is set to `CPLID`. |
| 6 | **Removed a stray file** | `OriginalImageOnWebDriver` is a 48-byte non-image text file containing only a Baidu-pan URL. It was removed. |
| 7 | **Standard packaging** | Added `data.yaml`, `classes.txt`, synchronized dual-format annotations (`Annotations/` + `labels/`) and refreshed per-split CSV tables. |

**Image pixels are never modified.** All 845 image files are byte-for-byte identical to the upstream files.

**Scope — structural clean-up only.** This is **not** a re-annotation. We separate,
de-duplicate and re-package the data; we do **not** re-verify individual boxes.
In particular, the **defective** images are **synthetic** — the original authors
generated them with the TVSeg + affine-augmentation + U-Net pipeline described
upstream and pasted the defects onto different backgrounds.

**Structural checks performed** on the released set:

| Check | Method | Result |
|---|---|---|
| Filename ↔ image ↔ XML pairing | bidirectional | **0** mismatches / orphans |
| Image size vs. XML `<size>` | pixel dimensions vs. header | **0** discrepancies (all 1152×864) |
| Out-of-range boxes | xmin/ymin ≥ 0, xmax ≤ W, ymax ≤ H | **0** |
| Degenerate boxes | zero / negative width or height | **0** |
| Cross-split duplicate images | byte-identical md5 across splits | **0** |
| Consecutive-frame leakage | +1 adjacency across splits | **0** |
| Class-name consistency | unique label set | 2 classes, no anomalies |

### Dataset at a glance

| Property | Value |
|---|---|
| Images | **845** (1152 × 864, RGB `.jpg`) |
| Classes | **2** — `insulator`, `defect` |
| Bounding boxes | **1,566** |
| Formats | Pascal VOC XML · YOLO TXT |
| Split | train **590** / val **129** / test **126** (seeded, class-stratified) |

**Boxes per class:** `insulator` 1,318 · `defect` 248

**Boxes per split:** train **1,104** (`insulator` 931 / `defect` 173) ·
val **237** (200 / 37) · test **225** (187 / 38)

### Structure

```
CPLID_Insulators-corrected/
├── images/{train,val,test}/      # 1152x864 RGB .jpg
├── labels/{train,val,test}/      # YOLO: cls cx cy w h (normalized)
├── Annotations/                  # Pascal VOC XML (flat, cleaned)
├── JPEGImages/                   # copy of the images (VOC layout)
├── csv/
│   ├── train_labels.csv          # filename,width,height,class,xmin,ymin,xmax,ymax
│   ├── val_labels.csv
│   └── test_labels.csv
├── classes.txt                   # 2 class names, one per line
├── data.yaml                     # Ultralytics YOLO configuration
├── LICENSE
└── README.md
```

Every image has exactly one YOLO `.txt` and one VOC `.xml`; there are **no empty label files**.

### Quick start (Ultralytics YOLO)

```bash
yolo detect train data=data.yaml model=yolov8n.pt epochs=100 imgsz=640
```

### Citation

**1. The original dataset — please always cite this.**

```bibtex
@article{tao2018detection,
  title   = {Detection of Power Line Insulator Defects Using Aerial Images
             Analyzed With Convolutional Neural Networks},
  author  = {Tao, Xian and Zhang, Dapeng and Wang, Zihao and Liu, Xilong
             and Zhang, Hongyan and Xu, De},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
  year    = {2018},
  publisher = {IEEE}
}
```

**2. This corrected release — please cite it as well.** It is not identical to the official
release: duplicates were removed, a reproducible split was added, the paired labels were
merged, and the VOC metadata was cleaned, so a citation to the original alone does not
describe this version.

```bibtex
@misc{cpld_insulators_corrected,
  author    = {KeenForgeAI},
  title     = {CPLID_Insulators-corrected: a cleaned release of the Chinese Power Line Insulator Dataset (CPLID)},
  year      = {2026},
  version   = {1.0},
  publisher = {KeenForgeAI},
  url       = {https://huggingface.co/datasets/KeenForgeAI/CPLID_Insulators-corrected},
  note      = {Curated by Lu Gan and Sam Li. Derived from the Chinese Power Line
               Insulator Dataset (CPLID; images from the State Grid Corporation of
               China, assembled by WANG Zi-Hao). Upstream licence unstated.}
}
```

**3. The annotation tool (optional).**

```bibtex
@software{keenforge,
  author    = {KeenForgeAI},
  title     = {KeenForge: a local-first, offline image annotation and model-training desktop tool},
  year      = {2026},
  publisher = {KeenForgeAI},
  url       = {https://github.com/KeenForgeAI/KeenForge},
  note      = {MIT licensed. Developed by Lu Gan and Sam Li.}
}
```

### License & attribution

**The upstream CPLID dataset states no license.** The repository ships no LICENSE file and
no terms of use. The normal images are credited to the State Grid Corporation of China and
the defective images are synthetic; the copyright status of the underlying images and
annotations is therefore **undetermined** — verify it before any commercial use.
See `LICENSE` in this repository.

Our modifications (de-duplication, split, label merging, format conversion, documentation)
are released under **CC BY 4.0**. Image pixels are unmodified and remain subject to the
upstream terms.

### Provenance

- Source: Chinese Power Line Insulator Dataset (CPLID), GitHub `InsulatorData/InsulatorDataSet`.
- Upstream: `Normal_Insulators` 600 img / 1,073 box · `Defective_Insulators` 248 img / 496 box (248 defect + 248 insulator).
- Verified: 845 images (1,566 boxes, byte-identical); 3 duplicates removed, 486 frame chains, 0 cross-split leakage, 0 invalid boxes.

---

## 中文

### 这是什么？

**中国电力线绝缘子数据集（CPLID）**的**清理、划分与标准化目标检测版**——包含无人机拍摄的
正常绝缘子图像，以及合成生成的缺陷绝缘子图像。图像由**国家电网公司**提供，数据集由
**王自浩（WANG Zi-Hao）** 整理。

论文：Tao, Zhang, Wang, Liu, Zhang & Xu，*《利用卷积神经网络分析航拍图像检测电力线绝缘子缺陷》*，
**IEEE Transactions on Systems, Man, and Cybernetics: Systems**，2018。
上游仓库：[InsulatorData/InsulatorDataSet](https://github.com/InsulatorData/InsulatorDataSet)。

原始版本以两个扁平、无划分的目录发布（`Normal_Insulators` 与 `Defective_Insulators`），其中
每张缺陷图被**标注了两次**（一次为缺陷，一次为整个绝缘子）并分别存放在两个平行的标签目录中；
VOC 元数据混入硬编码的个人绝对路径；压缩包内还留有一个垃圾非图片文件；并有 **3 张像素级完全重复**的图像。

本版本删除重复图、将同一段无人机连拍帧固定在同一划分内、把成对的标签目录合并为每图一份标注、
清理 VOC 元数据、剔除垃圾文件，提供可复现的**训练/验证/测试**划分，并附带开箱即用的
**YOLO TXT** 与 **Pascal VOC XML** 双格式标注及训练配置。

> ⚠️ **上游未声明许可证。** CPLID 未附带 LICENSE 文件，也无使用条款。详见
> [许可证与署名](#许可证与署名)——商用前请自行确认。

### 相对官方版的修正

| # | 修正 | 说明 |
|---|---|---|
| 1 | **删除 3 张重复图片** | `Normal_Insulators` 中有三对图像**像素级完全相同**（`0150` = `2984`、`0852` = `1876`、`1608` = `1938`）。本版删除每对中编号较大的一张，确保相同内容不会跨越新划分。 |
| 2 | **连拍帧固定同侧** | 600 张正常图均为无人机视频帧，其中 112 张与相邻帧编号连续。本版将其归并为 **486 个连拍链**（最长 5 帧），整条链**完整**分配到一个划分，避免近似重复帧被切散到 train/val/test。 |
| 3 | **合并成对标签目录** | 上游把每张缺陷图在两个平行目录中标注两次（`labels/defect/` + `labels/insulator/`）。本版合并为**每图一份 XML / 一份 YOLO**，同时包含绝缘子框与缺陷框。 |
| 4 | **可复现的训练/验证/测试划分** | 上游**完全没有划分**。本版基于连拍链分组，提供固定随机种子、按类别分层的 **70 / 15 / 15** 划分（随机种子 42）。 |
| 5 | **VOC 元数据清理** | 上游 XML 带有错误的 `<folder>` 值（`images`、`long`、空 混杂）以及硬编码的本地 `<path>`，暴露了含中文的个人绝对路径（如 `/media/laptop/vbox/硕士毕业设计/小论文/DataSet/...`）。均归一化，且 `<source><database>` 统一为 `CPLID`。 |
| 6 | **删除垃圾文件** | `OriginalImageOnWebDriver` 是一个 48 字节的非图片文本文件，内容仅为一个百度网盘链接，已删除。 |
| 7 | **规范化打包** | 新增 `data.yaml`、`classes.txt`、双格式同步标注（`Annotations/` + `labels/`）以及按划分刷新的 CSV 表格。 |

**图像像素从不修改**——全部 845 张图与上游文件逐字节一致。

**本版范围——仅结构化清理。** 这是**结构性清理，不是重新标注**。我们只做分离、去重、重新打包；
**不**重新核对标注框。特别说明：**缺陷图是合成的**——由原作者按上游所述的
TVSeg + 仿射增强 + U-Net 流程生成并贴到不同背景上。

**已执行的结构化检查**（针对发布的集合）：

| 检查 | 方法 | 结果 |
|---|---|---|
| 文件名↔图片↔XML 配对 | 双向 | **0** 错配 / 孤儿 |
| 图片尺寸 vs. XML `<size>` | 像素尺寸 vs. 头信息 | **0** 不一致（全部 1152×864） |
| 越界框 | xmin/ymin ≥ 0，xmax ≤ W，ymax ≤ H | **0** |
| 退化框 | 宽或高为零/负 | **0** |
| 跨划分重复图 | 跨划分逐字节 md5 | **0** |
| 连拍帧泄漏 | 跨划分 +1 邻接 | **0** |
| 类别名一致性 | 唯一类别名集合 | 2 类，无杂名 |

### 数据集概览

| 属性 | 值 |
|---|---|
| 图片 | **845**（1152 × 864，RGB `.jpg`） |
| 类别 | **2** —— `insulator`、`defect` |
| 标注框 | **1,566** |
| 格式 | Pascal VOC XML · YOLO TXT |
| 划分 | train **590** / val **129** / test **126**（固定种子、类别分层） |

**各类框数：** `insulator` 1,318 · `defect` 248

**各划分框数：** train **1,104**（`insulator` 931 / `defect` 173）·
val **237**（200 / 37）· test **225**（187 / 38）

### 目录结构

```
CPLID_Insulators-corrected/
├── images/{train,val,test}/      # 1152x864 RGB .jpg
├── labels/{train,val,test}/      # YOLO：cls cx cy w h（归一化）
├── Annotations/                  # Pascal VOC XML（扁平，已清理）
├── JPEGImages/                   # 图片副本（VOC 布局）
├── csv/
│   ├── train_labels.csv          # filename,width,height,class,xmin,ymin,xmax,ymax
│   ├── val_labels.csv
│   └── test_labels.csv
├── classes.txt                   # 2 个类别名称，每行一个
├── data.yaml                     # Ultralytics YOLO 配置文件
├── LICENSE
└── README.md
```

每张图片都有且仅有一个 YOLO `.txt` 与一个 VOC `.xml`，**不存在空标签文件**。

### 快速开始 (Ultralytics YOLO)

```bash
yolo detect train data=data.yaml model=yolov8n.pt epochs=100 imgsz=640
```

### 引用

使用本数据集请**同时引用**原始工作与本修正版：

**1. 原始数据集（请务必引用）** —— 见上方英文部分 `tao2018detection`。

**2. 本修正版（请一并引用）** —— 本版**与官方发布并不相同**：删除了重复图、补充了可复现划分、
合并了成对标签并清理了 VOC 元数据，因此**只引用原数据集无法描述本版本**。见上方英文部分
`cpld_insulators_corrected`。

**3. 标注工具（可选）** —— 见上方英文部分 `keenforge`。

### 许可证与署名

**上游 CPLID 数据集未声明许可证。** 仓库内无 LICENSE 文件、无使用条款。正常图由国家电网公司
提供，缺陷图为合成图像；因此原始图像与标注的**版权状态未定**——**商用前请自行确认**。
详见本仓库 `LICENSE`。

我们对数据集的修改部分（去重、划分、标签合并、格式转换、文档）以 **CC BY 4.0** 发布。
图像像素未修改，仍受上游条款约束。

### 来源

- 来源：中国电力线绝缘子数据集（CPLID），GitHub `InsulatorData/InsulatorDataSet`
- 上游：`Normal_Insulators` 600 图 / 1,073 框 · `Defective_Insulators` 248 图 / 496 框（248 缺陷 + 248 绝缘子）
- 已校验：845 图（1,566 框，逐字节一致）；删除 3 张重复图、486 个连拍链、0 跨划分泄漏、0 非法框