Here are a few use cases for this project:

1. Aircraft Maintenance and Safety Checks: This computer vision model could be used for conducting routine inspections of aircrafts for potential damages such as dents, scratches, or missing bolts. It could help eliminate human error and allow for much more detailed and accurate assessments.

2. Auto Repair and Inspection: The model could come in handy in auto repair shops to automate the process of identifying specific car damages. It can be employed for both pre and post-service inspection, ensuring that all missing bolts are replaced and dents repaired.

3. Quality Assurance in Painting Industries: Industries that deal with painting can apply this model to ensure quality in their painting process. The vision model can detect any paint-off from the object, notifying potential flaws that need correcting.

4. Automated Industrial Inspection: This model could be utilized in various industries during production, ensuring machines, and equipment are properly installed and not damaged. This could help mitigate potential accidents or operational disruptions in the manufacturing line.

5. Railway and Infrastructure Maintenance: The model can have its use in maintaining the safety of bridges, railways, and other constructed infrastructure by identifying any cracks, scratches or wear. This preemptive measure might prevent potential catastrophes caused by such infrastructure failures.

Dataset

This dataset was created by Suryansh Gupta

Contents

Here are a few use cases for this project:

1. Aviation Maintenance: The model can be used to automate the inspection routine of aircraft cockpits and other parts of the aircraft, detecting those five common structural damages. Early detection and subsequent repair can contribute to safer and more efficient aviation operations.

2. Automobile Industry: The AI model can be applied to assess and inspect the condition of cars in production lines or used cars, identifying any imperfections such as dents, cracks, scratches or paint-offs before the car goes to market.

3. Building Inspection: In civil engineering, the model could be used to monitor the structural health of buildings or bridges, using the crack and dent detection capabilities to timely identify potential structural issues.

4. Insurance Claim Processing: Insurance companies could use this model to streamline their claim processing by automatically identifying damage in pictures of insured properties like cars, homes or commercial properties, that have been submitted for claims.

5. Artwork Preservation: Art galleries and museums could use this model to identify early signs of damage on art pieces (paint-off or cracks) and take preventative measures to help save valuable pieces of art.

With the increasing integration of functional imaging techniques like Positron Emission Tomography (PET) into radiotherapy (RT) practices, a paradigm shift in cancer treatment methodologies is underway. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, novel tumour control methods, such as intensity modulated radiation therapy (IMRT) dose painting, demand the precise delineation of multiple intensity value contours to ensure optimal tumour dose distribution. Recently, convolutional neural networks (CNNs) have made significant strides in 3D image segmentation tasks, most of which present the output map at a voxel-wise level. However, because of information loss in subsequent downsampling layers, they frequently fail to precisely identify precise object boundaries. Moreover, in the context of dose painting strategies, there is an imperative need for reliable and precise image segmentation techniques to delineate high recurrence-risk contours. To address these challenges, we introduce a 3D coarse-to-fine framework, integrating a CNN with a kernel smoothing-based probability volume contour approach (KsPC). This integrated approach generates contour-based segmentation volumes, mimicking expert-level precision and providing accurate probability contours crucial for optimizing dose painting/IMRT strategies. Our final model, named KsPC-Net, leverages a CNN backbone to automatically learn parameters in the kernel smoothing process, thereby obviating the need for user-supplied tuning parameters. The 3D KsPC-Net exploits the strength of KsPC to simultaneously identify object boundaries and generate corresponding probability volume contours, which can be trained within an end-to-end framework. The proposed model has demonstrated promising performance, surpassing state-of-the-art models when tested against the MICCAI 2021 challenge dataset (HECKTOR).

Dataset

This dataset was created by Shashil

Contents

The Booknis dataset is a collection of manually annotated images featuring medieval singing representations for segmentation and object detection. These images are derived from manuscripts that date back to the period between the 12th and 15th centuries and consist of various genres, including liturgical, secular, musical, scientific, and historical works. The dataset includes five classes, namely books, altars, lecterns, sheets, and phylacteries, which serve as indicators of potential musical representation. We have meticulously annotated 341 images and 1513 annotations, which musicology experts have verified. The primary objective of Booknis is to advance research on transfer learning by providing a more challenging benchmark for few-shot object detection.

We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. In contrast to previous work, we abandon the use of computationally involved adversarial objectives, network ensembles and style transfer. Instead, we employ standard data augmentation techniques − photometric noise, flipping and scaling − and ensure consistency of the semantic predictions across these image transformations. We develop this principle in a lightweight self-supervised framework trained on co-evolving pseudo labels without the need for cumbersome extra training rounds. Simple in training from a practitioner's standpoint, our approach is remarkably effective. We achieve significant improvements of the state-of-the-art segmentation accuracy after adaptation, consistent both across different choices of the backbone architecture and adaptation scenarios.

Automated coastline extraction from optical satellites is fundamental to coastal mapping, and sea-land segmentation is the core technology of coastline extraction. Deep convolutional neural networks (DCNNs) have performed well in semantic segmentation in recent years. However, sea-land segmentation using deep learning techniques remains a challenging task, due to the lack of a benchmark dataset and the difficulty of deciding which semantic segmentation model to use. We present a comparative framework of sea-land segmentation to Landsat-8 OLI imagery via semantic segmentation in deep learning techniques. Three issues are investigated: (1) constructing a sea-land benchmark dataset using Landsat-8 Operational Land Imager (OLI) imagery consisting of 18,000 km2 of coastline around China; (2) evaluating the feasibility and performance of sea-land segmentation by comparing the accuracy assessment, time complexity, spatial complexity and stability of state-of-the-art DCNNs methods; (3) choosing the most suitable semantic segmentation model for sea-land segmentation in accordance with Akaike information criterion (AIC) and Bayesian information criterion (BIC) model selection. Results show that the average test accuracy achieves over 99% accuracy, and the mean Intersection over Unions (mean IoU) is above 92%. These findings demonstrate that the Fully Convolutional DenseNet (FC-DenseNet) performs better than other state-of-the-art methods in sea-land segmentation, based on both AIC and BIC. Considering training time efficiency, DeeplabV3+ performs better for sea-land segmentation. The sea-land segmentation benchmark dataset is available at: https://pan.baidu.com/s/1BlnHiltOLbLKe4TG8lZ5xg.

Most semantic segmentation works have obtained accurate segmentation results through exploring the contextual dependencies. However, there are several major limitations that need further investigation. For example, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. Moreover, local convolutions are commonly used in deep learning models to learn attention and capture local patterns in the data. These convolutions operate on a small neighborhood of the input, focusing on nearby information and disregarding global structural patterns. To address these concerns, we propose a Global Domain Adaptation Attention with Data-Dependent Regulator (GDAAR) method to explore the contextual dependencies. Specifically, to effectively capture both the global distribution information and local appearance details, we suggest using a stacked relation approach. This involves incorporating the feature node itself and its pairwise affinities with all other feature nodes within the network, arranged in raster scan order. By doing so, we can learn a global domain adaptation attention mechanism. Meanwhile, to improve the features similarity belonging to the same segment region while keeping the discriminative power of features belonging to different segments, we design a data-dependent regulator to adjust the global domain adaptation attention on the feature map during inference. Extensive ablation studies demonstrate that our GDAAR better captures the global distribution information for the contextual dependencies and achieves the state-of-the-art performance on several popular benchmarks.

The acrylic paints market is growing at an average pace and is anticipated to record a CAGR of 4.5 % from 2022 to 2032.

Attributes	Details
Acrylic Paints Market Value in 2020	US $ 117.7 Million
Acrylic Paints Market CAGR (2022 to 2032)	4.5 %

Scope of Report

Attributes	Details
Forecast Period	2022 to 2032
Historical Data Available For	2015 to 2020
Market Analysis	US$ Million for Value
By Type	Heavy Body Acrylics Fluid Acrylics Interactive Open Acrylics
By Application	Fabrics Wood Metal Ceramics Walls Paintings
By End Use	Building and Construction Textile Home Furnishing Educational Institutions
By Region	North America Latin America Western Europe Eastern Europe Japan Asia Pacific Middle East & Africa
Key Players	Azkonobe Asian Paints India BASF SE Berger Paints India Berkshire Hathaway

The authors of the PASCAL Context dataset conduct a comprehensive investigation into the significance of context within existing state-of-the-art detection and segmentation methodologies. Their approach involves the meticulous labeling of every pixel encompassed within the PASCAL VOC 2010 detection challenge, associating each pixel with a semantic category. This dataset is envisioned to present a considerable challenge to the research community, as it incorporates an impressive 520 additional classes that cater to both semantic segmentation and object detection.

Here are a few use cases for this project:

1. Infrastructure Maintenance: The model can be used by government agencies or private companies to assess the condition of roads, bridges, and buildings in real-time. Regular scans can help detect emerging cracks, and consequently, worrisome structural issues in their early stages - leading to preventive maintenance.

2. Construction Quality Assurance: Construction firms can use the model to check and ensure the integrity of their work. The model can be used to inspect walls, floors, and other structures for cracks that indicate possible construction faults.

3. Safety Inspections: The model can be useful for companies dealing with safety inspections, such as fire departments or safety regulators, to identify cracks in various types of infrastructure like pipelines, chemical plants, or nuclear facilities that may pose accident risks.

4. Geological Study: Geological or seismological researchers can use this model to identify and categorize cracks in geological structures for analysis, potentially aiding in predicting earthquakes or land shifts.

5. Art Restoration: Museums or art restoration firms can use the model to detect and monitor cracks in artwork over time, aiding in the preservation and restoration process.

Market.us announces the publication of its most recently generated research report titled, “Global Painting Machines Market by Product Type (Paint Sprayers and Automatic Painting Machine), By Application (Industrial Production, Automobile & Aerospace Industry, Furniture & Decoration, Architecture), and by Region – Global Forecast to 2028.†, which offers a holistic view of the global painting machines market through systematic segmentation that covers every aspect of the target market.

The global painting machine market is projected to be US$ 2,792.4 Mn in 2021 to reach US$ 4,888.4 Mn by 2028 at a CAGR of 8.1%. Read More

Paint and Coatings Market Size

The size of the global paint and coatings market will increase nearly USD 45 billion between 2016-2021, exceeding USD 187 billion in market size by the end of the forecast period. The global paint and coatings market will grow at a CAGR of almost 6% by 2021, owing to the growth of the housing and construction sector. The housing market and the construction industry are witnessing a significant growth owing to a rise in population, subsequently fueling the need for paints and coatings. Additionally, growth in the DIY market, growing consumption of nano-coatings, the introduction of fluoropolymer topcoats, and novel developments in paints and coatings market are the key trends expected to gain traction in the market through 2021.

Several emerging trends are expected to gain traction and positively impact this market during the forecast period. These top trends include the growth in the DIY market, the growing consumption of nano-coatings, the introduction of fluoropolymer topcoats, and novel developments in the paints and coatings market. Other insights provided within our paint and coatings market reports include:

Paint and Coating Market Insights

The architectural segment which includes interior and exterior house paint, primers, stains and sealers, and undercoats will be major contributors to the overall market growth. The demand for paints and coatings in interior wall applications will drive the growth of the decorative paints and coatings market through 2021.



Rising awareness about eco-friendly products will be a major factor positively impacting the growth of decorative paints and coating market. Water-based paint and coatings offer durability, quick dry time, less emission of volatile organic compounds, and less odor.



The rapid expansion of the automotive industry will drive the growth prospects for the global automotive paints and coatings market. The demand for light and medium weight vehicles is growing due to the increasing income of middle-class consumers. This rapid growth has led to the growth in the production of lightweight materials to be used in all varieties of vehicles like a passenger, commercial, and green energy vehicles.

View more paint and coatings market research insights: Download a free sample report now

Paint and Coating Market Share and Segmentation

Within our paint and coatings industry research, we provide deep insights into the market landscape, its segments, and their market share. Our research experts segment this market by resin type, technology, and application. By resin type, the acrylic resins segment accounts for the largest market shareholding a lead over the epoxy, alkyd, and polyurethane resin segments. The acrylic resins market was valued at USD 47.66 billion in 2016.

Our paint and coating market segmentation research offerings include:

Paint <table cellpadding='10' cellspacing='0' style='border-collapse:collapse; margin-bottom:10px; margin-left:1px; width:99%'> <tbody> <tr> <td colspan='2' style='text-align:center; vertical-align:middle'> <p><strong>Scope of Structural Steel</strong></p> </td> </tr> <tr> <td style='background-color:#2ab570; border-color:#d9d9d9; border-style:solid; border-width:1px; height:65px; vertical-align:middle; width:450px'> <p style='text-align:left'><span style='color:white'>Report Specs</span></p> </td> <td style='background-color:#2ab570; border-color:#d9d9d9; border-style:solid; border-width:1px; vertical-align:middle; width:750px'> <p style='text-align:left'><span style='color:white'>Details</span></p> </td> </tr> <tr> <td style='background-color:#f2f2f2; border-color:#d9d9d9; border-style:solid; border-width:1px; height:40px; vertical-align:middle; width:450px'> <p style='text-align:left'><span style='color:black'>Page number</span></p> </td> <td style='background-color:#f2f2f2; border-color:#d9d9d9; border-style:solid; border-width:1px; vertical-align:middle; width:750px'> <p style='text-align:left'><span style='color:black'>120</span></p> </td> </tr> </tbody> </table>

Most semantic segmentation works have obtained accurate segmentation results through exploring the contextual dependencies. However, there are several major limitations that need further investigation. For example, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. Moreover, local convolutions are commonly used in deep learning models to learn attention and capture local patterns in the data. These convolutions operate on a small neighborhood of the input, focusing on nearby information and disregarding global structural patterns. To address these concerns, we propose a Global Domain Adaptation Attention with Data-Dependent Regulator (GDAAR) method to explore the contextual dependencies. Specifically, to effectively capture both the global distribution information and local appearance details, we suggest using a stacked relation approach. This involves incorporating the feature node itself and its pairwise affinities with all other feature nodes within the network, arranged in raster scan order. By doing so, we can learn a global domain adaptation attention mechanism. Meanwhile, to improve the features similarity belonging to the same segment region while keeping the discriminative power of features belonging to different segments, we design a data-dependent regulator to adjust the global domain adaptation attention on the feature map during inference. Extensive ablation studies demonstrate that our GDAAR better captures the global distribution information for the contextual dependencies and achieves the state-of-the-art performance on several popular benchmarks.

Large-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications.
​This microscopy data set includes 919,265 five-channel fields of view representing 30,616 tested compounds, available at The Cell Image Library repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied.
Because computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The data set can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically-perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.
The current version of the dataset was generated using updated CellProfiler pipelines to improve the quality of cell and nucleus segmentations. To evaluate the quality of segmentations, 30 wells were randomly sampled across seven plate maps from the bioactive compound collection, and one site was randomly sampled per well, producing a test set of 210 five-channel images. Based on this set, two expert CellProfiler users produced an improved segmentation pipeline that was used to reprocess all the 406 plates. The updated pipeline also produces more measurements per cell (n=1783) compared to the previous version. This version of the dataset contains 7 fewer plates 406 compared to 413). Six of these (PlateIDs = 26782, 26783, 26784, 26791, 26792, 26796) correspond to plates that had later been reimaged and should not have been included in analysis. One set (25723) had several inconsistencies in the files that could not be resolved, and was therefore excluded.

Car Semantic Segmentation Vehicle dataset

The dataset consists of a collection of car photos that have been manually annotated with masks. Each mask uses special colors to clearly mark the boundaries of different regions in the image.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on TrainingData to buy the datase

DATASETS WITH VEHICLES :

• License Plates - 1 209 438 OCR Plates
• Road Segmentation Dataset
• Car Object Detection & Tracking
• Parking Space Detection & Classification Dataset
• Videos around cars

Car Photos

The car images were collected in-house on city streets in clear weather conditions, with natural shooting angles. The dataset includes both passenger cars and commercial vehicles.

We at TrainingData offer image collection services tailored to your specific needs, delivering tens of thousands of images within a short time.

Semantic Segmentation

The masks represent a semantic segmentation of car photos. The following parts of the car are highlighted on the masks: - bumper - fog lights - radiator - license plate - door handle - and others.

The complete list of highlighted elements and the assigned colors of the car are available in the masks_info.csv file.

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/data-market to discuss your requirements, learn about the price and buy the dataset

TrainingData also provides high-quality data annotation tailored to your needs.

keywords: vehicle segmentation dataset, semantic segmentation for self driving cars, self driving cars dataset, semantic segmentation for autonomous driving, car segmentation dataset, car dataset, car images, car parts segmentation, self-driving cars deep learning

Auction sale catalogues macro segmentation

Document layout analysis dataset for segmenting the macro structure of sale catalogues.

We follow SegmOnto controlled vocabulary (https://segmonto.github.io/) and the COLaF (Inria, ALMAnaCH and Multispeech) schema.

Dataset

Two random folio have been selected from 8 auction sale catalogues collections, kept in the national library of France (Bibliothèque nationale de France, BnF), and the national institute for art history (Institut national d'histoire de l'art, INHA).

• Bienaimé-Feuardent (BnF)
• Bourgey (BnF)
• Dubois (BnF)
• Naville (BnF)
• Rollin (BnF)
• XVIIIe (BnF)
• Desvouges (INHA)
• Lair-Dubreuil (INHA)

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Robotic Paint Booth Market 2023-2027

The Robotic Paint Booth Market size is estimated to grow at a CAGR of 5.7% between 2022 and 2027. The market size is forecast to increase by USD 1,215.19 million. The growth of the market depends on several factors, such as the growing focus on safeguarding the health of industrial workers, the growing demand for automobiles in emerging economies, and the growing emphasis on reducing paint wastage and improving resource utilization.

This report extensively covers market segmentation by product (paint booths and paint robots), end-user (automotive and non-automotive), and geography (APAC, Europe, North America, South America, and Middle East and Africa). It also includes an in-depth analysis of drivers, trends, and challenges.

What will be the size of the Robotic Paint Booth Market During the Forecast Period?

Robotic Paint Booth Market Forecast 2023-2027

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Robotic Paint Booth Market Dynamics

Our researchers studied the data for years, with 2022 as the base year and 2023 as the estimated year, and presented the key drivers, trends, and challenges for the market. Although there has been a disruption in the growth of the market during the COVID-19 pandemic, a holistic analysis of drivers, trends, and challenges will help companies refine marketing strategies to gain a competitive advantage.

Driver- Growing emphasis on reducing paint wastage and improving resource utilization

There is an increasing need for sustainable and efficient painting processes in companies across many industries such as automotive, aerospace, and manufacturing. Additionally, there is a growing concern regarding paint wastage not only due to environmental implications but also as a significant cost factor. The main disadvantage of the traditional painting method is that it leads to overspray and inconsistent application resulting in significant paint consumption and disposal.

Therefore, there is an increasing adoption of robotic pain booths to address such concerns across several industries. The main advantage of these robotic paint booths is that can significantly reduce paint wastage by optimizing the application process with precision and accuracy. Additionally, These systems ensure even and controlled coating, reducing overspray and ensuring that each product receives the exact amount of paint required. Hence, such factors are positively impacting the market. Thus, it is expected to drive the market growth during the forecast period.

Trends - Increased adoption of environment-friendly robotic paint booths

The adoption of robotic paint booths involves high upfront costs as well as high energy consumption. Additonally, a large volume of space is required for the the installation of robotic paint booths. Therefore, there is an increasing focus by market players to develop eco-friendly robotic paint booths that can function efficiently and incur lower costs without unnecessary wastage of resources.

For example, Durr, a prominent market player in the robotic paint booth market, has developed the Eco+ line of robotic paint booths. there is an increasing application of this robotic paint booth across all domains from pre-treatment, techniques like electro-dip painting, and new concepts, such as heating ovens and exhaust air purification systems. Hence, such factors are positively impacting the market which in turn will drive the market growth during the forecast period.

Challenge - Lack of skilled workers

The shortage of skilled workers can pose a significant threat to the global l robotic paint booth market. This lack of skilled work is an impact of critical impediment to the industry's growth and efficiency. There has been a rising demand for robotic paint booths across different sectors, including automotive, aerospace, and industrial manufacturing.

However. there is a lack of technicians who can effectively program, control, and troubleshoot these advanced machines. It has become a significant challenge for companies to find qualified individuals with the needed technical expertise to ensure the smooth operation of robotic paint booths. Hence, such factors are negatively impacting the market. Therefore, it is expected to hinder the market growth during the forecast period.

Robotic Paint Booth Market Segmentation by Product, End-user and Geography

Product Segment Analysis

The paint booths segment is estimated to witness significant growth during the forecast period. Paint booth systems, paint exhaust systems, or spray-painting booths can be referred to as the structured layout where the components are painted without any contamination from the external surroundings. These structures are fully automated and have the ability to handle a broad range of products from small metal components to big aircraft and spaceships. In addition, paint booth