KolektorSDD2 is a surface-defect detection dataset with over 3000 images containing several types of defects, obtained while addressing a real-world industrial problem.

The dataset consists of:

356 images with visible defects 2979 images without any defect image sizes of approximately 230 x 630 pixels train set with 246 positive and 2085 negative images test set with 110 positive and 894 negative images several different types of defects (scratches, minor spots, surface imperfections, etc.)

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).

Metal Surface Defect Detection

## Overview

Metal Surface Defect Detection is a dataset for object detection tasks - it contains Defects annotations for 2,310 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 Danielfi Nez

Contents

Surface Defect Detection

## Overview

Surface Defect Detection is a dataset for object detection tasks - it contains Defects annotations for 1,175 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).

Surface Defect Detection System Market Outlook

In 2023, the global surface defect detection system market size was valued at approximately USD 4.5 billion, and it is projected to reach USD 10.5 billion by 2032, with a robust CAGR of 9.5% from 2024 to 2032. The remarkable growth of this market can be attributed to the increasing demand for quality assurance and automation in manufacturing processes. The growing integration of advanced technologies such as Machine Vision and Artificial Intelligence (AI) for defect detection is significantly propelling market growth.

One of the primary growth factors for the surface defect detection system market is the booming manufacturing and industrial sectors. As industries strive for higher reliability and quality in their products, the need for advanced defect detection technologies becomes paramount. The automation of quality control processes enabled by surface defect detection systems not only enhances efficiency but also reduces human error, leading to significantly improved product quality and consistency. This high demand for quality assurance drives the adoption of such systems across various sectors, thereby fueling market growth.

Furthermore, technological advancements in AI and Deep Learning are revolutionizing the surface defect detection system market. These technologies enable more accurate and faster detection of defects by analyzing vast amounts of data and identifying patterns that are not easily discernible by human inspection. The integration of AI with machine vision systems enhances their capability to detect even the most subtle and complex defects, thereby increasing their utility across diverse applications. As a result, industries are increasingly investing in these advanced systems to stay competitive and ensure product excellence.

The increasing stringency of regulatory standards related to product quality and safety also plays a crucial role in the market's expansion. Regulatory bodies across various regions are imposing stricter guidelines to ensure that products meet high-quality standards and are free from defects. Compliance with these regulations necessitates the adoption of surface defect detection systems, as they provide reliable and precise inspection capabilities. This regulatory push further accentuates the importance of these systems in maintaining product integrity and consumer safety, thereby contributing to market growth.

Regionally, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid industrialization, particularly in countries like China and India, coupled with the increasing adoption of automation in manufacturing processes, is driving the demand for surface defect detection systems in the region. North America and Europe also hold significant market shares due to the presence of advanced manufacturing facilities and stringent quality standards in these regions. These regional dynamics highlight the diverse opportunities and growth potential across different geographical areas.

Component Analysis

The surface defect detection system market is segmented into three primary components: Hardware, Software, and Services. The hardware segment includes cameras, sensors, and other detection devices that are crucial for capturing images and data necessary for defect detection. The software segment comprises the algorithms and applications that analyze the captured data to identify defects. Services include installation, maintenance, and after-sales support provided by vendors to ensure the efficient operation of the systems.

The hardware segment dominates the market due to the extensive use of high-resolution cameras and advanced sensors in defect detection systems. These components are essential for capturing detailed images and data that are analyzed to detect surface defects. Technological advancements in hardware, such as the development of high-speed cameras and 3D sensors, are further enhancing the capabilities of defect detection systems. The continuous innovation in hardware components is expected to drive their demand, thereby contributing significantly to the market's growth.

The software segment is also experiencing substantial growth, driven by the integration of AI and machine learning algorithms in defect detection systems. These advanced software solutions enable more accurate and efficient defect detection by analyzing data and identifying patterns that are not easily detectable by traditional methods. The increasing adoption of cloud-based software solutions is further

malleable cast iron surface defect dataset(MCISD)

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.

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 (temporarily only the first one is available). 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. 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 1.4% of the total image area 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_currents.csv that contains the numbers roughly corresponding to the light intensity.

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.

NG samples are regarded as positive samples

Panel Surface Defect Detection

## Overview

Panel Surface Defect Detection is a dataset for object detection tasks - it contains Letters annotations for 2,528 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).

The authors of the Severstal: Steel Defect Detection dataset acknowledge that steel holds a paramount position as one of the most vital building materials in modern construction. Its resilience against both natural elements and wear caused by human activities has rendered it indispensable worldwide. In the pursuit of enhancing the efficiency of steel production, the Severstal competition aims to play a pivotal role in the detection of defects within the steel production process.

The global surface defect detection system market, valued at $1181 million in 2025, is projected to experience robust growth, driven by increasing automation in manufacturing, stringent quality control standards across diverse industries, and the rising adoption of advanced imaging techniques like AI-powered vision systems. The 7.6% CAGR from 2019 to 2033 indicates a significant expansion, with the market expected to surpass $2000 million by 2033. Key drivers include the need for enhanced product quality, reduced production costs through early defect detection, and the growing demand for non-destructive testing methods. The market's segmentation likely includes technologies (e.g., machine vision, laser scanning, X-ray inspection), industry verticals (e.g., automotive, electronics, pharmaceuticals), and deployment types (e.g., inline, offline). Competitive pressures are expected to remain high due to the presence of numerous established players and emerging technology providers. Growth is being fueled by several factors. Advancements in machine learning and artificial intelligence are enabling more accurate and efficient defect detection, leading to improved productivity and lower scrap rates. Furthermore, the integration of surface defect detection systems into Industry 4.0 initiatives is accelerating adoption, particularly among large-scale manufacturers seeking greater process optimization and real-time data analysis. However, the market might face certain restraints such as high initial investment costs associated with advanced systems and the need for specialized technical expertise for implementation and maintenance. Nevertheless, the long-term benefits in terms of quality improvement, cost reduction, and enhanced operational efficiency are expected to outweigh these challenges, ensuring continued market expansion throughout the forecast period.

This is a synthetic dataset for defect detection on textured surfaces. It was originally created for a competition at the 2007 symposium of the DAGM (Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., the German chapter of the International Association for Pattern Recognition). The competition was hosted together with the GNSS (German Chapter of the European Neural Network Society).

After the competition, the dataset has been used as a test dataset in multiple projects and research papers. It is publicly available from the University of Heidelberg website (Heidelberg Collaboratory for Image Processing).

The data is artificially generated, but similar to real world problems. The first six out of ten datasets, denoted as development datasets, are supposed to be used for algorithm development. The remaining four datasets, which are referred to as competition datasets, can be used to evaluate the performance. Researchers should consider not using or analyzing the competition datasets before the development is completed as a code of honour.

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.

The global surface defect detection system market is experiencing robust growth, projected to reach a market size of $1723 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 7.9% from 2019 to 2033. This expansion is driven by several key factors. Increasing automation across various industries, particularly in manufacturing (automotive, semiconductors, and electronics), necessitates precise and efficient quality control. The demand for higher-quality products with reduced defects is a significant driver, pushing companies to adopt advanced surface defect detection systems. Furthermore, technological advancements in image processing and non-destructive testing (NDT) techniques are enabling the development of more accurate, faster, and cost-effective solutions. The integration of AI and machine learning algorithms further enhances the capabilities of these systems, leading to improved defect identification and classification. Growing adoption of Industry 4.0 principles and the increasing need for real-time monitoring and analysis are further propelling market growth. Market segmentation reveals a strong presence across diverse applications, with the automotive, semiconductor, and electronics sectors showing significant demand. Based on technology, image processing-based systems currently dominate the market due to their versatility and accuracy, although NDT methods are gaining traction owing to their suitability for specific applications. Geographically, North America and Europe are currently leading the market, driven by early adoption of advanced technologies and stringent quality standards. However, rapid industrialization and economic growth in Asia-Pacific are expected to fuel significant market expansion in this region in the coming years, presenting lucrative opportunities for market players. The presence of established players like AMETEK, Nordson, and others, coupled with the emergence of innovative startups, indicates a competitive yet dynamic market landscape. Challenges remain, including high initial investment costs and the need for skilled personnel to operate and maintain these sophisticated systems. However, ongoing technological innovation and increasing affordability are expected to mitigate these challenges in the long term.

The dataset is constructed from images of defective production items that were provided and annotated by Kolektor Group d.o.o.. The images were captured in a controlled industrial environment in a real-world case.

The dataset consists of 399 images at 500 x ~1250 px in size.

Please cite our paper published in the Journal of Intelligent Manufacturing when using this dataset:

@article{Tabernik2019JIM, author = {Tabernik, Domen and {\v{S}}ela, Samo and Skvar{\v{c}}, Jure and Sko{\v{c}}aj, Danijel}, journal = {Journal of Intelligent Manufacturing}, title = {{Segmentation-Based Deep-Learning Approach for Surface-Defect Detection}}, year = {2019}, month = {May}, day = {15}, issn={1572-8145}, doi={10.1007/s10845-019-01476-x} }

To encourage research in computer vision for the railway, we present Rail-5k: a real-world image dataset for object detection of defects and accessories on the rail, along with methods for shooting, fine-frained category definition, and instance-level annotation. We collected 5,000 high-quality RGB images from high-speed railway and subway across China, where each image with resolution as high as 0.03mm per pixel. We annotate 1100 images with 13 types of defects and accessories that are the most important to rail maintenance such as rail surface, wheel-rail contact band, crack, spalling, corrugation, fastening, screw. The dataset is superior to existing datasets in image quantity, resolution, annotation quality, dense and small objects. It also contains real-world corrupted images with dark, overexposure, blur, other tools, different lens distance, category transition, different screws, which are infeasible for non-experts to annotate and recognize. As a pilot study of rail defect detection, we perform comprehensive experiments using SOTA models. Our experiments demonstrate several challenges Rail-5k posed to both computer vision and railway engineering. Future versions of this dataset will include even more images, segmentation annotations as well as more channels.

601 images of four types of aircraft fuselage defects. The camera was used to photograph different parts of the aircraft fuselage in different lighting environments.

Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detecting defects (mismatch), general defects (bubble, glass-crack and cell-crack) and easy-detecting defects (glass-upside-down). Corresponding to different types of defects, the deep learning model with different optimization methods and a classification detection method based on multi-models fusion are proposed in the paper. In the proposed model, in order to solve the mismatch problem between the default anchor boxes size of YOLOv5s model and the extreme scale of the battery mismatch defect label boxes, the K-means algorithm was adopted to re-cluster the dedicated anchor boxes for the mismatch defect label boxes. In order to improve the comprehensive detection accuracy of YOLOv5s model for the general defects, the YOLOv5s model was also improved by the methods of image preprocessing, anchor box improving and detection head replacing. In order to ensure the recognition accuracy and improve the detection speed for easy-detecting defects, the lightweight classification network MobileNetV2 was also used to classify the cells with glass-upside-down defects. The experimental results show that the proposed optimization model and classification detection method can significantly improve the defect detection precision. Respectively, the detection precision for mismatch, bubble, glass-crack and cell-crack defects are up to 95.64%, 91.8%, 93.1% and 98.0%. By using lightweight model to train the glass-upside-down defect dataset, the average classification accuracy reaches 100% and the detection speed reaches 13.29 frames per second. The comparison experiments show that the proposed model has a great improvement in detection accuracy compared with the original model, and the defect detection speed of lightweight classification network is improved more obviously, which confirms the effectiveness of the proposed optimization model and the multi-defect classification detection method for solar cells defect detection.