--- 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` 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](#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 `` value (`new` / `result2`), a hard-coded local `` exposing a personal Windows path (`C:\Users\86151\Desktop\...`), and `Unknown`. 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 `` | 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) ```bash yolo detect train data=data.yaml model=yolov8n.pt epochs=100 imgsz=640 ``` ### Citation **1. The original dataset / paper — please always cite this.** ```bibtex @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. ```bibtex @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).** ```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 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](https://github.com/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 `` 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](#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 `` 值（`new` / `result2`）、泄露个人 Windows 路径的硬编码 ``（`C:\Users\86151\Desktop\...`）以及 `Unknown`。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 `` | 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) ```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 / 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