This dataset contains the train/validation split for the NEU Steel Surface Detection Dataset. The original dataset can be found here => https://www.kaggle.com/datasets/rdsunday/neu-urface-defect-database The source also contains the description of the dataset and the type of images it contains.

Accompanying paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8007367/ Accompanying blog post: https://debuggercafe.com/steel-surface-defect-detection/

In short: The dataset contains close up of images of steel surface defects. There are 6 classes into which the defects can be classified. They are: [ 'crazing', 'inclusion', 'patches', 'pitted_surface', 'rolled-in_scale', 'scratches' ]

You can find more details in the paper.

Total samples: 1800 Training samples: 1700 Validation samples: 100

Annotations are in XML format.

This dataset was downloaded from the NEU Metal Surface Defects Database. It collects six kinds of typical surface defects of hot-rolled steel strips: rolled-in scale (RS) patches (Pa) crazing (Cr) pitted surface (PS) inclusion (In) scratches (Sc) The database includes 1,800 grayscale images and 300 samples, each of six typical surface defects.

C10-DET represents a dataset designed for the detection of defects on large-scale metallic surfaces. It poses significant challenges in terms of the variety of defect categories, the number of images, and data volume. The presence of surface defects on metallic materials can have detrimental effects on the quality of industrial products, making efficient detection of metallic defects essential to meet the quality standards set by various industries. Consequently, there has been a growing interest in the field of metallic surface defect detection, leading to substantial advancements in quality control for industrial applications. However, the task of identifying metallic surface defects is inherently complex, primarily due to environmental factors like lighting, light reflections, and the unique properties of metal materials. These factors significantly increase the intricacy of surface defect detection. Note, that while the original paper stated that the dataset contained 3,570 grayscale images, the current version offers 2,300 images.

Metallic Surface Defect Detection

## Overview

Metallic Surface Defect Detection is a dataset for object detection tasks - it contains Defects annotations for 2,295 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is part of the DAGM 2007 (Deutsche Arbeitsgemeinschaft für Mustererkennung) dataset series, specifically Class10, which focuses on industrial surface defect detection. It is widely used for training and benchmarking machine learning models in computer vision-based quality control systems, including CNNs, attention models, and reinforcement learning agents.

The dataset contains high-resolution grayscale images with and without defects, along with labeled ground truth. Class10 is known for its challenging textures and subtle defect patterns, making it ideal for research on robust and adaptive inspection systems.

This dataset has been cited in various works, including research on weakly supervised learning, attention mechanisms, and real-time defect detection.

Citation: M. Wieler, T. Hahn, and F. A. Hamprecht, “Weakly Supervised Learning for Industrial Optical Inspection,” Heidelberg Collaboratory for Image Processing (HCI), Sep. 14, 2007. DOI: 10.5281/zenodo.12750201

Metal Surface Defect Detection 1

## Overview

Metal Surface Defect Detection 1 is a dataset for object detection tasks - it contains Surface Defects Hj0i QnpH Surface Defects 93Ko annotations for 11,048 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Kaustubh Dixit

Contents

In the development of surface defect detection algorithms for steel, benchmark datasets play a crucial role. However, the widely used benchmark datasets generally suffer from limited scale, insufficient coverage of defect categories, and poor annotation accuracy, making it difficult to meet the growing demand for surface defect detection in steel. To this end, this study constructed and annotated a large-scale, multi-scale benchmark dataset SD10 (Steel Defect Detection) covering 10 typical types of defects: Crazing, Inclusion, Patches, Pitted Surface, Rolled in Scale, Scratches, Blowhole, Break, Fray, and Uneven. The dataset aims to break through the application bottleneck of existing datasets, provide high-quality and reliable data support for in-depth research and optimization iteration of steel surface defect detection algorithms.

Steel Surface Defect Detection

## Overview

Steel Surface Defect Detection is a dataset for instance segmentation tasks - it contains Steel Surface Defect Detection annotations for 1,769 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Automated Optical Inspection (AOI) technology plays a significant role in industrial defect detection.However, traditional static optical systems are affected by shadows and surface reflectivity, resulting in a high sensitivity to the direction of the illuminant, false positives, and missed detections, especially for metal parts with complex geometries. Moreover, there is still a lack of large-scale datasets for surface defect detection in such scenarios. To address these issues, an automatic metal surface defect detection technique was proposed based on deep learning and photometric stereo vision, and a Metal Surface Defect Dataset (MSDD) was constructed. Firstly, a Stroboscopic Illuminant Image Acquisition (SIIA) method is proposed, which incorporates a specially designed arrangement of illuminants and a channel mixer to blend the collected multi-channel images into RGB pseudo-color images. Secondly, the MSDD is constructed using this technology. We achieve end-to-end surface defect detection using universal object detectors by mapping color space transformations to spatial domain transformations and employing hue randomization for data augmentation. Finally, four universal object detection methods, including FCOS, YOLOv5, YOLOv8, and RT-DETR are validated on this dataset. The results indicate that these models achieve an average precision of 85.4% on the dataset, significantly outperforming traditional methods. The MSDD consists of a total of 138,585 single-channel images and 9,239 mixed images, including 5746 defect-free images and 3493 images containing a total of eight types of defects. The defect patterns included are generally applicable for the automated visual inspection of casting-formed metal blank surface defects, highlighting its high research value.

Credits - Zhou, Qianyu (2023), “GKN Blade Surface Defect Dataset”, Mendeley Data, V1, doi: 10.17632/3bh998k78g.1

Detect the defects of the blade between good, nicks and scratches. The blade Surface Defect detection dataset is a multiclass image classification dataset that can be used to detect the defects in the blades produced.

In automated surface visual inspection, it is often necessary to capture the inspected part under many different illumination conditions to capture all the defects. To address this issue, at CSEM we have acquired a real-world multi-illumination defect segmentation dataset, called CSEM-MISD and we release it for research purposes to benefit the community.

The dataset consists of three different types of metallic parts -- washers, screws, and gears. Parts were captured in a half-spherical light-dome system that filtered out all the ambient light and successively illuminated it from 108 distinct illumination angles. Each 12 illumination angles share the same elevation level and the relative azimuthal difference between the adjacent illumination angles on the same level is 30 degrees. For more details, please read Sections 3 and 4 of our paper.

The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.

We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.

This dataset is challenging mainly because:

each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space;

the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors;

the annotations are not very precise because the exact extent of defect contours is always subjective;

the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation.

The dataset is organized as follows:

each sample resides in the Test, Train, or Unannotated directory;

each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks;

each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels;

Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12).

Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.

We provide data loaders implemented in python at the project's repository.

If you find our dataset useful, please cite our paper:

Honzátko, D., Türetken, E., Bigdeli, S. A., Dunbar, L. A., & Fua, P. (2021). Defect segmentation for multi-illumination quality control systems. Machine vision and Applications.

This dataset provides synthetic samples of surface defects generated on a CAD model of a car door. The defects include bumps and peaks, simulated using Free-Form Deformation (FFD) to ensure geometric realism and adaptability to curved surfaces. Surface acquisition is emulated using a virtual 3D profilometric sensor, incorporating both geometric and sensor noise to closely replicate real-world inspection conditions.

All samples are labeled, and the dataset includes depth images, trajectory data, and raw sensor outputs, making it suitable for training and evaluating surface defect detection models in industrial settings.

This dataset is associated with the TriPlay repository on GitHub:
🔗 GitHub Repository

It is also related with the following publication:

📄 Simulation of Laser Profilometer Measurements in the Presence of Speckle Using Perlin Noise

(This dataset is also associated with a manuscript currently under review.)

🔑 Key Features

• High-Quality Synthetic Defects: Includes localized surface deformations (bumps and peaks) modeled with Free-Form Deformation.

• Virtual Profilometric Scanning: Simulates data acquisition with a 3D profilometer to capture realistic sensor readings.

• Realistic Sensor Noise: Adds surface and depth distortion to simulate real acquisition conditions.

• Per-Step Trajectory and Sensor Data: Includes detailed trajectory files and raw outputs per scanning step.

• Automatically Generated Annotations: Bounding boxes and defect metadata are included for supervised learning.

Surface Defects Detection

## Overview

Surface Defects Detection is a dataset for object detection tasks - it contains Defects annotations for 1,799 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Synthetic Industrial Metal Surface Defect Dataset

Overview

A procedurally generated dataset of 15,000 grayscale images depicting industrial metal surfaces with five distinct surface conditions. Purpose-built for training and benchmarking computer vision models in manufacturing quality control applications.

https://img.shields.io/badge/Images-15%2C000-blue" alt="Images"> https://img.shields.io/badge/Classes-5-green" alt="Classes"> https://img.shields.io/badge/Resolution-256%C3%97256-orange" alt="Resolution"> https://img.shields.io/badge/Format-PNG-lightgrey" alt="Format"> https://img.shields.io/badge/Size-%7E390MB-purple" alt="Size">

Why This Dataset?

Defect detection in manufacturing is critical for quality assurance, but real-world defect images are: - ❌ Expensive to collect - ❌ Highly imbalanced (defects are rare events) - ❌ Often proprietary or restricted - ❌ Difficult to reproduce

This synthetic dataset provides a balanced, fully reproducible alternative for developing and testing defect detection algorithms—with no licensing restrictions.

Classes

Class	Description	Train	Val	Total
Normal	Defect-free metal surface with natural texture	2,400	600	3,000
Scratch	Linear abrasion marks (Bezier curves)	2,400	600	3,000
Crack	Branching fracture patterns (L-system)	2,400	600	3,000
Rust	Corrosion patches (Perlin noise blobs)	2,400	600	3,000
Hole	Puncture defects with depth shading	2,400	600	3,000
Total		12,000	3,000	15,000

Key Features

✅ Perfectly balanced classes (3,000 images each)
✅ Pre-split into train (80%) and validation (20%)
✅ Rich metadata with 14 features per image
✅ 100% reproducible via seed-based generation (master seed: 42)
✅ CPU-generated using pure NumPy/SciPy—no GAN, no GPU required
✅ Realistic variations in texture type, lighting angle, and noise

Dataset Structure

├── images/
│  ├── train/          # 12,000 images (80%)
│  │  ├── normal/        # 2,400 images
│  │  ├── scratch/       # 2,400 images
│  │  ├── crack/        # 2,400 images
│  │  ├── rust/         # 2,400 images
│  │  └── hole/         # 2,400 images
│  └── val/           # 3,000 images (20%)
│    ├── normal/        # 600 images
│    ├── scratch/       # 600 images
│    ├── crack/        # 600 images
│    ├── rust/         # 600 images
│    └── hole/         # 600 images
├── metadata.csv         # Per-image generation metadata
├── config.json          # Full generation configuration
└── README.md           # Dataset documentation

Image Specifications

Property	Value
Resolution	256 × 256 pixels
Color Mode	Grayscale (L mode, single channel)
Format	PNG (lossless compression)
Bit Depth	8-bit (0-255)
File Naming	{class}_{index:05d}.png

Metadata Schema

Each image has detailed generation metadata in metadata.csv:

Column	Type	Description
filename	string	Image filename (e.g., scratch_00042.png)
class	string	Defect class label
split	string	train or val
width	int	Image width (256)
height	int	Image height (256)
base_intensity	int	Mean grayscale intensity (90-190)
texture_type	string	Surface texture: brushed, matte, or grain
lighting_angle	float	Directional lighting angle (0-360°)
noise_strength	float	Applied noise intensity (0.02-0.20)
defect_count	int	Number of defects (0 for normal)
defect_coverage_pct	float	Percentage of image affected by defects
defect_positions	list	Pixel coordinates of defect centers
defect_sizes_px	list	Size of each defect in pixels
generation_seed	int	Seed for exact reproducibility
generation_timestamp	datetime	When the image was generated

Use Cases

🎯 Image Classification

Train CNNs, Vision Transformers (ViT), or traditional ML classifiers to distinguish between defect types.

🔍 Anomaly Detection

Use "normal" images for one-class classifier training; evaluate on defective samples.

🔬 Transfer Learning

Pre-train on this synthetic data, then fine-tune on real manufacturing images.

📊 Benchmarking

Compare model architectures with a standardized, balanced dataset.

🧪 Data Augmentation Research

Study how synthetic data complements limited real-world datasets.

Quick Start

PyTorch

from torchvision import datasets, transforms
from torch.utils.data import DataLoader

transform = transforms.C...

Context

Surface defect inspection and detection within narrow, hollow cylindrical surfaces i.e., pipes and barrels can enormously impact the structural integrity of industrial products. Defect detection can play a major role in building inspection, finding minor defects to prolong the product's life.

Content

The dataset contains images of inside cylindrical surfaces. All images are in a single folder. The image folder contains 1,071 channel 3 images. The annotations for each image are recorded in seperate XML files. for five types of defects i.e., dirt, rusting, pitting, chipping, and thermal cracking.

Surface Defect Detection: dataset & papers

🐎📈 Constantly summarizing open source data sets in the field of surface defect research is very important. Important critical papers from year 2017 have been collected and compiled, which can be viewed in the :open_file_folder: [Papers] folder. 🐋

At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios.

Compared with the clear classification, detection and segmentation tasks in computer vision, the requirements for defect detection are very general. In fact, its requirements can be divided into three different levels: "what is the defect" (classification), "where is the defect" (positioning) And "How many defects are" (split).

1. Key Issues in Surface Defect Detection

1）Small Sample Problem

The current deep learning methods are widely used in various computer vision tasks, and surface defect detection is generally regarded as its specific application in the industrial field. In traditional understanding, the reason why deep learning methods cannot be directly applied to surface defect detection is because in a real industrial environment, there are too few industrial defect samples that can be provided.

Compared with the more than 14 million sample data in the ImageNet dataset, the most critical problem faced in surface defect detection is small sample problem. In many real industrial scenarios, there are even only a few or dozens of defective images. In fact, for the small sample problem which is one of the key problems in industrial surface defect detection, there are currently 4 different solutions:

- Data Amplification and Generation

The most commonly used defect image expansion method is to use multiple image processing operations such as mirroring, rotation, translation, distortion, filtering, and contrast adjustment on the original defect samples to obtain more samples. Another more common method is data synthesis, where individual defects are often fused and superimposed on normal (non-defective) samples to form defective samples.

- Network Pre-training and Transfer Learning

Generally speaking, using small samples to train deep learning networks can easily lead to overfitting, so methods based on pre-training networks or transfer learning are currently one of the most commonly used methods for samples.

- Reasonable Network Structure Design

The need for samples can also be greatly reduced by designing a reasonable network structure. Based on the compressed sampling theorem to compress and expand small sample data, we use CNN to directly classify the compressed sampling data features. Compared with the original image input, compressing the input can greatly reduce the network's demand for samples. In addition, the surface defect detection method based on the twin network can also be regarded as a special network design, which can greatly reduce the sample requirement.

- Unsupervised or Semi-supervised Method

In the unsupervised model, only normal samples are used for training, so there is no need for defective samples. The semi-supervised method can use unlabeled samples to solve the network training problem in the case of small samples.

2）Real-time Problem

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Surface Defect Detection Market Outlook

According to our latest research, the global Surface Defect Detection market size reached USD 3.18 billion in 2024, reflecting robust adoption across industries such as automotive, electronics, and manufacturing. The market is poised for significant expansion, projected to grow at a CAGR of 8.7% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 6.73 billion. This impressive growth trajectory is fueled by rapid advancements in artificial intelligence (AI) and machine vision technologies, which are increasingly being integrated into quality control processes to enhance productivity and minimize operational costs.

One of the primary growth drivers for the Surface Defect Detection market is the escalating demand for automation and stringent quality assurance standards within manufacturing industries. Companies are prioritizing defect-free products to maintain competitive advantage and comply with international quality regulations. The integration of advanced technologies such as deep learning and machine vision into inspection systems has revolutionized defect detection, enabling real-time analysis and high accuracy in identifying surface irregularities. These innovations not only reduce human error but also facilitate faster and more reliable inspection processes, which are critical in high-volume production environments.

Another significant factor contributing to market growth is the increasing complexity of manufactured products, particularly in sectors like electronics, automotive, and aerospace. As products become more sophisticated, the need for highly sensitive and precise surface defect detection systems intensifies. Manufacturers are investing heavily in research and development to create solutions capable of detecting micro-defects that were previously undetectable using traditional methods. Furthermore, the proliferation of smart factories and Industry 4.0 initiatives is accelerating the deployment of surface defect detection solutions, enabling seamless integration with existing production lines and data analytics platforms for continuous process improvement.

The rising adoption of cloud-based deployment models and the growing trend of digital transformation are also propelling the Surface Defect Detection market. Cloud-based solutions offer scalability, remote accessibility, and centralized data management, which are increasingly important for multinational corporations operating in multiple locations. The availability of AI-powered analytics and real-time monitoring capabilities via the cloud enhances decision-making and allows for predictive maintenance, reducing downtime and operational costs. This shift towards cloud deployment is particularly prominent among large enterprises seeking to optimize their quality control processes and leverage big data analytics for strategic advantage.

Regionally, Asia Pacific stands out as the dominant force in the Surface Defect Detection market, driven by its strong manufacturing base, particularly in China, Japan, and South Korea. The region’s rapid industrialization, coupled with government initiatives supporting smart manufacturing, has led to widespread adoption of advanced inspection systems. North America and Europe are also significant contributors, with a strong focus on technological innovation and compliance with strict quality standards in sectors such as automotive and aerospace. Meanwhile, emerging markets in Latin America and the Middle East & Africa are gradually recognizing the benefits of surface defect detection technologies, with increasing investments in automation and quality control infrastructure.

Component Analysis

The Component segment of the Surface Defect Detection market is categorized into hardware, software, and services, each playing a pivotal role in the overall ecosystem. Hardware components, including cameras, sensors, lighting systems, and image acquisition devices, form the backbone of defect detection systems. The demand for high-resolution cameras and advanced sensors has surged, driven by the need for precise and reliable detection of minute surface flaws. Manufacturers are continuously upgrading hardware capabilities to support higher speeds and resolutions, enabling real-time inspection even in challenging industrial environments. The hardware segment currently holds the largest share of the market, owing to the critical importance of

each measuring 160×1000 pixels

Dataset Description: WoodDefect Detection Domain: Automated visual inspection in hardwood processing Task: Multi-class defect detection with bounding-box labels Origin and acquisition The images were collected by GoldenY through a high-resolution industrial camera under controlled diffuse LED illumination in a European sawmill. The original footage is publicly shared on the Roboflow Universe platform under the “CC-BY 4.0” licence. All data were taken from the planed surface of kiln-dried European beech (Fagus sylvatica) boards with thickness 26–50 mm. No magnification filters or digital zoom were applied, guaranteeing a native pixel resolution of ≈ 0.08 mm px⁻¹ on the wood surface. Volume and splits Total annotated images: 3 773. Current partitioning: – Training: 2 641 images (70 %) – Validation: 566 images (15 %) – Test: 566 images (15 %) The split is stratified so that the global defect-class distribution is preserved within each subset.