Dataset Statistics Tool

This directory contains the dataset statistics tool and the report files it generates. Keeping the script, JSON summary, Markdown report, and small visual assets in one place makes the data audit easy to rerun and review.

Dataset parsing is shared through data_tools/utils/. The statistics tool imports ELPV, PV-Multi-Defect, PVEL-AD, and Pascal VOC helpers from that package instead of keeping private loader code inside this report folder.

Dataset Scope

The project uses three public solar-defect datasets because each one answers a different modeling question.

Dataset	Image type	Label type	Main task	What the model should output
ELPV Dataset	Single-cell electroluminescence images	One defect probability per image	Image classification or anomaly scoring	A probability, class, or anomaly score for the whole cell image.
PV-Multi-Defect	Panel images with visible surface defects	Pascal VOC bounding boxes	Object detection on panel-level defects	Box coordinates and defect class names for visible damaged regions.
PVEL-AD	Near-infrared EL cell images	Pascal VOC bounding boxes plus normal or auxiliary images	Long-tail defect detection and anomaly analysis	Box coordinates and one of 12 manufacturing-defect classes.

ELPV Dataset

ELPV is used when the question is whether a single solar cell looks defective. The input is a normalized grayscale EL image. The label is not a box; it is a defect probability such as 0.0, 0.3333333333333333, 0.6666666666666666, or 1.0. A normal result for this dataset is a clean image count, a readable probability distribution, and sample images that open as 300 by 300 cell crops.

PV-Multi-Defect

PV-Multi-Defect is used when the question is where a visible panel defect appears. The input is a panel image. The label is one or more Pascal VOC boxes, with classes such as broken, hot_spot, black_border, scratch, and no_electricity. A normal result is a non-empty XML parse, class counts with visible imbalance, and sampled images where the defect class is visually plausible.

PVEL-AD

PVEL-AD is used for the main long-tail detection track. The input is a near-infrared EL image of a photovoltaic cell. The released box labels cover 12 defect classes: finger, crack, black_core, thick_line, horizontal_dislocation, short_circuit, vertical_dislocation, star_crack, printing_error, corner, fragment, and scratch. A normal result is a full image count of 36,543, released VOC annotations for the trainval and test subsets, and a class table where frequent classes and rare classes are both visible.

What It Does

build_dataset_report.py reads the local datasets under datasets/raw/ and produces:

Output	Path	Purpose
Machine-readable statistics	dataset_stats.json	Lets later training or validation scripts reuse the same counts.
English report	dataset_report.md	Explains the dataset scale, label formats, class distribution, and sanity checks.
Chinese report	dataset_report.zh.md	Same report for Chinese documentation.
Generated figures	assets/	Bar charts and sampled image grids created from local files.
Source examples	datasets/raw/...	A small allowlisted set of original project images kept for documentation display. The full datasets remain ignored.

Original Project Images

This section keeps a small set of original display images from the dataset projects in datasets/raw/. They provide source-level visual context before the generated statistics and sampled grids.

From ELPV Dataset

The ELPV project describes the dataset as solar-cell crops extracted from high-resolution electroluminescence images of photovoltaic modules. It contains 2,624 normalized 300 x 300 grayscale samples from 44 modules. Each sample has a defect probability between 0 and 1 and a module type label, either mono- or polycrystalline.

Source description: the ELPV overview colors cell images by defect likelihood. A darker red overlay means a higher probability that the solar cell contains a defect.

From PV-Multi-Defect

The PV-Multi-Defect project provides panel images in JPEGImages/ and Pascal VOC labels in Annotations/. Its source README describes five visible defect examples: broken areas, bright spots, black or gray border areas, scratches, and non-electricity black areas.

Source description: photovoltaic panels with broken areas.

Source description: photovoltaic panels with obvious bright spot areas.

Source description: photovoltaic panels with black or gray border areas.

Source description: photovoltaic panels with scratched areas.

Source description: photovoltaic panels that have non-electricity regions and show black areas.

From PVEL-AD

The PVEL-AD project describes a large near-infrared EL dataset for photovoltaic cell anomaly detection. It contains 36,543 images, anomaly-free samples, and anomalous samples from 12 defect categories. The released annotations make it a long-tail object detection task, because frequent classes such as finger interruption appear much more often than rare classes such as scratch or fragment.

Source description: the PVEL-AD project presents the dataset as a photovoltaic electroluminescence anomaly detection dataset with anomaly-free cells and 12 defect categories, including crack, star crack, finger interruption, black core, thick line, scratch, fragment, corner, printing error, horizontal dislocation, vertical dislocation, and short circuit.

Source description: the image is used by the PVEL-AD project to show the near-infrared EL visual style of photovoltaic cell defects. In this project it is kept only as a source-data reference image; generated sample grids and class-count plots are produced separately by build_dataset_report.py.

How To Run

Run the script from the project root:

python3 data_tools/stats/build_dataset_report.py

The input is the local ignored dataset tree:

datasets/raw/

The output is this directory:

data_tools/stats/

The run is normal when it finishes without parser errors, dataset_stats.json is updated, and the Markdown reports show real images rather than broken links.