Datasets:

KeenForgeAI
/

PV_Multi_Defect-corrected

license: other
license_name: see-license-file-upstream-unstated
license_link: LICENSE
task_categories:
  - object-detection
language:
  - en
  - zh
tags:
  - photovoltaic
  - pv-panel
  - solar-panel
  - defect-detection
  - industrial-inspection
  - renewable-energy
  - object-detection
  - pascal-voc
  - yolo
  - keenforge
size_categories:
  - 1K<n<10K

PV_Multi_Defect-corrected

Photovoltaic (PV) panel multi-defect detection — cleaned, split and standardized version of the PV-Multi-Defect dataset. 光伏组件多缺陷检测 —— PV-Multi-Defect 数据集的清理、划分与规整版。

English · 中文

English

What is this?

A cleaned, split and standardized object-detection version of the PV-Multi-Defect dataset — photovoltaic panel imagery with 5 defect classes: broken areas, hot spots (bright spots), black/gray borders, scratches, and non-electrified (black) areas. Upstream repository: houhou34/PV-Multi-Defect.

Reference: Li, L., Wang, Z. & Zhang, T. (2023). "GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection." Electronics, 12(3), 561 — https://doi.org/10.3390/electronics12030561.

The original release ships as a flat, unsplit JPEGImages/ + Annotations/ pair (Pascal VOC, 600×600 grayscale, no split), containing 61 groups of byte-identical duplicate images, 2 images with no annotation and 1 annotation with no objects, and VOC metadata polluted with a hard-coded personal Windows path and a bogus <folder> value.

This release removes the duplicates and orphan files, keeps the one near-duplicate cluster on a single side of the split, cleans the VOC metadata, provides a reproducible train/val/test split, and ships ready-to-train Pascal VOC XML + YOLO TXT annotations with configuration files.

⚠️ Upstream license: none stated. The repository ships no LICENSE file. The associated article is open access (MDPI). See License & attribution — verify before commercial use.

Corrections vs. the official release

#	Correction	Detail
1	Removed 61 duplicate images	61 pairs are byte-identical (md5). The redundant copy of each pair was removed so identical content cannot straddle the new split.
2	Removed orphan / empty files	2 images had no annotation (`img3059`, `img9513`) and 1 annotation (`img7863`) contained no objects; all 3 were dropped.
3	Near-duplicate cluster kept together	One cluster of 12 perceptually identical images (8×8-dHash ≤ 2 and 32×32 pixel-MSE ≤ 10) is assigned entirely to one split, so it cannot leak across train/val/test.
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 near-duplicate groups.
5	VOC metadata clean-up	The upstream XML carried a bogus `<folder>` value (`new` / `result2`), a hard-coded local `<path>` exposing a personal Windows path (`C:\Users\86151\Desktop\...`), and `<database>Unknown</database>`. All are normalised.
6	Dual format + packaging	Added YOLO TXT from the same coordinates alongside the upstream VOC XML, plus `classes.txt`, `data.yaml` and per-split CSV tables.

Image pixels are never modified. All 1,044 images are byte-for-byte identical to the upstream files (600×600, single-channel grayscale).

Scope — structural clean-up only. This is not a re-annotation. We de-duplicate, drop orphans and re-package the data; we do not re-verify individual boxes.

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 600×600)
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
EXIF orientation anomalies	tag scan	0

Dataset at a glance

Property	Value
Images	1,044 (600 × 600, single-channel grayscale `.jpg`)
Classes	5 — `black_border`, `broken`, `hot_spot`, `no_electricity`, `scratch`
Bounding boxes	3,403
Formats	Pascal VOC XML · YOLO TXT
Split	train 723 / val 155 / test 166 (seeded, class-stratified)

Boxes per class: hot_spot 1,553 · scratch 1,349 · black_border 251 · no_electricity 158 · broken 92

Boxes per split: train 2,358 · val 519 · test 526

Defect types (from the upstream README): broken areas · obvious bright-spot areas · black or gray border areas · scratched areas · non-electrified (black) areas.

Structure

PV_Multi_Defect-corrected/
├── images/{train,val,test}/      # 600x600 grayscale .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                   # 5 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)

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

Citation

1. The original dataset / paper — please always cite this.

@article{li2023gbh,
  title   = {GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target
             Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection},
  author  = {Li, Longlong and Wang, Zhifeng and Zhang, Tingting},
  journal = {Electronics},
  volume  = {12},
  number  = {3},
  pages   = {561},
  year    = {2023},
  publisher = {MDPI},
  doi     = {10.3390/electronics12030561}
}

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

@misc{pv_multi_defect_corrected,
  author    = {KeenForgeAI},
  title     = {PV_Multi_Defect-corrected: a cleaned release of the PV-Multi-Defect photovoltaic panel defect dataset},
  year      = {2026},
  version   = {1.0},
  publisher = {KeenForgeAI},
  url       = {https://huggingface.co/datasets/KeenForgeAI/PV_Multi_Defect-corrected},
  note      = {Curated by Lu Gan and Sam Li. Derived from the PV-Multi-Defect
               dataset (Li, Wang & Zhang, Electronics 2023). Upstream licence unstated.}
}

3. The annotation tool (optional).

@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 PV-Multi-Defect dataset states no license. The GitHub repository ships no LICENSE file and no terms of use; the associated article is published open access by MDPI. 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, orphan removal, split, metadata clean-up, format conversion, documentation) are released under CC BY 4.0. Image pixels are unmodified and remain subject to the upstream terms.

Provenance

Source: PV-Multi-Defect dataset (Pascal VOC), GitHub houhou34/PV-Multi-Defect.
Upstream: 1,108 images / 1,106 XML / 3,981 objects; no split; 61 duplicate groups; 2 unannotated images; 1 empty annotation.
Verified: 1,044 images (3,403 boxes, byte-identical); 61 duplicates removed, 3 orphan/empty files dropped, 0 cross-split leakage, 0 invalid boxes.

中文

这是什么？

PV-Multi-Defect 数据集的清理、划分与标准化目标检测版——光伏组件图像，覆盖 5 类缺陷：破损区域、亮斑（热斑）、黑/灰边框、划痕、无电（黑区）。上游仓库： houhou34/PV-Multi-Defect。

论文：Li, L., Wang, Z. & Zhang, T. (2023).《GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection》，Electronics 12(3), 561 —— https://doi.org/10.3390/electronics12030561。

原始版本以扁平的、无划分的 JPEGImages/ + Annotations/（Pascal VOC，600×600 灰度，无划分）发布，包含 61 组逐字节完全重复的图片、2 张无标注图片与 1 个无目标的标注文件，且 VOC 元数据混入了硬编码的个人 Windows 路径与错误的 <folder> 值。

本版本删除重复与孤儿文件、把唯一的近重复簇固定到同一划分、清理 VOC 元数据、提供可复现的训练/验证/测试划分，并附带开箱即用的 Pascal VOC XML + YOLO TXT 双格式标注及配置文件。

⚠️ 上游未声明许可证。 仓库无 LICENSE 文件；关联论文由 MDPI 开放获取。详见许可证与署名——商用前请自行确认。

相对官方版的修正

#	修正	说明
1	删除 61 张重复图	有 61 对图片逐字节完全相同（md5）。本版删除每对中多余的副本，确保相同内容不会跨越新划分。
2	删除孤儿/空文件	2 张图片没有标注（`img3059`、`img9513`），1 个标注文件（`img7863`）不含任何目标；三者均已剔除。
3	近重复簇固定同侧	唯一一个由 12 张感知近似图组成的簇（8×8 dHash ≤ 2 且 32×32 像素 MSE ≤ 10）整组分配到一个划分，不会跨越 train/val/test 泄漏。
4	可复现的训练/验证/测试划分	上游完全没有划分。本版基于近重复分组，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（`new` / `result2`）、泄露个人 Windows 路径的硬编码 `<path>`（`C:\Users\86151\Desktop\...`）以及 `<database>Unknown</database>`。均归一化。
6	双格式 + 规范化打包	由同一坐标生成 YOLO TXT（与上游 VOC XML 并行），并补充 `classes.txt`、`data.yaml` 与按划分的 CSV 表格。

图像像素从不修改——全部 1,044 张图与上游文件逐字节一致（600×600，单通道灰度）。

本版范围——仅结构化清理。 这是结构性清理，不是重新标注。我们只做去重、剔除孤儿、重新打包；不重新核对单个标注框。

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 600×600）
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
EXIF 方向异常	标签扫描	0

数据集概览

属性	值
图片	1,044（600 × 600，单通道灰度 `.jpg`）
类别	5 —— `black_border`、`broken`、`hot_spot`、`no_electricity`、`scratch`
标注框	3,403
格式	Pascal VOC XML · YOLO TXT
划分	train 723 / val 155 / test 166（固定种子、类别分层）

各类框数： hot_spot 1,553 · scratch 1,349 · black_border 251 · no_electricity 158 · broken 92

各划分框数： train 2,358 · val 519 · test 526

缺陷类型（引自上游 README）： 破损区域 · 明显亮斑区域 · 黑/灰边框区域 · 划痕区域 · 无电（黑区）。

目录结构

PV_Multi_Defect-corrected/
├── images/{train,val,test}/      # 600x600 灰度 .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                   # 5 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)

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 / 论文（请务必引用） —— 见上方英文部分 li2023gbh。

2. 本修正版（请一并引用） —— 本版与官方发布并不相同：删除了重复与孤儿、补充了可复现划分、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 pv_multi_defect_corrected。

3. 标注工具（可选） —— 见上方英文部分 keenforge。

许可证与署名

上游 PV-Multi-Defect 数据集未声明许可证。 其 GitHub 仓库无 LICENSE 文件、无使用条款；关联论文由 MDPI 开放获取。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、剔除孤儿、划分、元数据清理、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：PV-Multi-Defect 数据集（Pascal VOC），GitHub houhou34/PV-Multi-Defect
上游：1,108 图 / 1,106 XML / 3,981 框；无划分；61 组重复；2 张未标注图；1 个空标注
已校验：1,044 图（3,403 框，逐字节一致）；删除 61 张重复、剔除 3 个孤儿/空文件、0 跨划分泄漏、0 非法框