This dataset was originally created by Vanitchaporn. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/vanitchaporn/circuit-gexit.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by Yuyang Li. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/yuyang-li/tower_jointv1.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by St Hedgehog Yusupov. To see the current project, which may have been updated since this version, please go here.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This is an Instance Segmentation project for visualizing detected cracks on concrete. This dataset is usable for those doing transportation and public safety studies, creating self-driving car models, or testing out computer vision models for fun.

• Featured Transportation Projects

Check out this example guide from Augmented Startups to see the model in action and learn how the dataset came together: https://medium.com/augmented-startups/yolov7-segmentation-on-crack-using-roboflow-dataset-f13ae81b9958

This dataset was originally created by Monika Patel, Kartik Attri, Aniket Dhanotia, Divyam Jha, Pankaj, kanchan, Ujjwal Sharma, Garvita Vijay, Aniket Choudhary, Pearl Rathour, Roshni Ghai, Kavya Shukla, Preeti Sharma, Ananya Kharayat, Krishna Gambhir, Lav Naruka, Kas, Sejal, Tejasvi Singh, Ayush Sahu, Pri, Aniket Dhanotia, and Devansh Shrivastava. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/new-workspace-rzrja/pcb-2.0.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by Djamel Mekhlouf, Abrisse Cerine, Anfal Lanna, Yasmin Emekhlouf. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/annotationericsson/annotation-2.0.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by Ahmad Rabiee. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/ahmad-rabiee/asbest91/.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by Khaingwintz. To see the current project, which may have been updated since this version, please go here.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by Evan Kim, MJ Kim. To see the current project, which may have been updated since this version, please go here.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

Here are a few use cases for this project:

1. Smart Building Design and Analysis: Architects and engineers could use the Windows Instance Segmentation model to automatically analyze building facades in images and identify the distribution, sizes, and styles of windows. This information can be used to improve building designs for daylighting, ventilation, and aesthetic purposes.

2. Real Estate Appraisal and Listing: Real estate professionals can use the model to analyze property photos, automatically identifying and categorizing windows to create more detailed and accurate property listings. Potential buyers and renters can then use this information for better search results and understanding of architectural features.

3. Energy Efficiency Analysis: Energy consultants and researchers can utilize the Windows Instance Segmentation model to analyze the prevalence of different window styles and their impact on building energy efficiency. This can help in developing more sustainable building designs and energy retrofit strategies.

4. Urban Planning and Cityscape Analysis: Urban planners and city officials can make use of this model to assess the distribution of windows in urban environments, understanding how they contribute to the overall aesthetic and livability of neighborhoods. This information can guide zoning regulations and future development projects to create more visually appealing and functional cities.

5. Augmented Reality (AR) Applications: Developers of AR applications, particularly those focused on architecture and interior design, can integrate the Windows Instance Segmentation model to recognize windows in real-world environments. This can enable users to visualize new window styles, treatments, or decorations, helping them make better-informed design decisions.

Here are a few use cases for this project:

1. Urban Planning and Development: Utilizing the "Buildings Instance Segmentation" model to analyze aerial images of a city, urban planners can identify different types of buildings and their distribution to make informed decisions about zoning, infrastructure, and future developments.

2. Damage Assessment and Emergency Response: In the aftermath of natural disasters, the model can be used to analyze aerial images to quickly identify damaged or destroyed buildings, helping emergency responders prioritize rescue efforts and allocate resources more efficiently.

3. Real Estate Market Analysis: Real estate professionals can use the model to analyze aerial views of neighborhoods, identifying different types of buildings and their locations to offer better insights into neighborhood characteristics and trends for potential property buyers.

4. Energy Efficiency and Environmental Impact Analysis: By identifying different building classes and their distribution, researchers can evaluate energy consumption patterns and develop strategies for improving energy efficiency and reducing the environmental impact in urban areas.

5. Historical Preservation and Cultural Heritage: The model can be employed to identify and track the presence of culturally significant or historically important buildings for preservation efforts, ensuring their protection and integration into urban development plans.

This dataset contains images of 3D printer failures across a variety of print jobs spanning several types of printer, material, models, and colors. It can be used as the starting point for creating a computer vision model that monitors a print job and aborts it as soon as it becomes evident that there is a problem (and alerts you before wasting a ton of time and money on materials). It can also be used to do automated quality assurance on finished models to make sure they do not exhibit common defects.

A dataset of 276 images is provided, along with a pre-trained model you can try in your browser and deploy to several different edge devices.

Here are a few use cases for this project:

1. Law Enforcement and Security: The License Plate Recognition model can be employed by law enforcement agencies to automatically detect and record license plates of vehicles involved in traffic violations, criminal activities or search for stolen vehicles.

2. Parking Management: This model can be utilized by parking lot operators to automate the entry, exit and vehicle records, facilitating seamless parking experience for users and enabling efficient parking management.

3. Toll Collection: Highways and toll plazas can implement the License Plate Recognition system to automatically identify and track vehicles passing through toll booths, enhancing the convenience for drivers and expediting the toll collection process.

4. Access Control: Business parks, gated communities, and private properties can use the License Plate Recognition model to enable automatic access or authentication for authorized vehicles, providing secure and hassle-free entry.

5. Traffic Monitoring and Statistics: Transportation authorities can employ this model to gather real-time data about traffic, monitor congestion, and analyze traffic patterns, providing valuable insights for urban planning and infrastructure development.

This project uses an Object Detection model to detect CRACKS to any infrastructure like tall buildings, skyscraper, bridges etc. using footage from Drone.

Here are a few use cases for this project:

1. Compliance Monitoring: The Construction Site Safety model can be used by construction site managers, safety officers, or regulatory agencies to monitor and ensure that workers are adhering to safety protocols, such as wearing appropriate personal protective equipment (PPE).

2. Accident Detection and Prevention: The model can be integrated with surveillance or monitoring systems on construction sites to detect potentially hazardous situations, such as a person not wearing a hardhat or safety vest near heavy machinery, allowing for real-time intervention and accident prevention.

3. Construction Site Access Control: The model can be employed at entry and exit points of construction sites to identify and grant access only to authorized personnel wearing the proper safety gear, helping to maintain a safe working environment and prevent unauthorized access.

4. Equipment and Vehicle Tracking: The Construction Site Safety model can be used to automatically track the movement and usage of construction vehicles and machinery within the construction site, enabling better project management, fleet optimization, and maintenance scheduling.

5. Job Site Documentation and Reporting: The model can be employed in generating documentation and reports on the compliance, safety measures, and progress of construction projects. It can automatically label photos taken of the construction site, providing valuable metadata for site inspections, progress tracking, and safety audits.

This project labels solar panels collected via a DJI Mavic Air 2 flying over Rancho Santa Fe, California in August 2022. Both rooftop and backyard solar panels are labeled. It was used as the basis for the Using Computer Vision with Drones for Georeferencing blog post and the open source DJI aerial georeferencing project.

53 images labeled with 267 polygons were used to train a computer vision model to detect solar panels from above. It's a demonstration of collecting and annotating data from a drone video and using that to train a machine learning model.

Here are a few use cases for this project:

1. Insurance Claims Processing: Car_Dent_Scratch_Detection(1) can be utilized by insurance companies to streamline their claims processing by automatically detecting, assessing, and documenting car damage. The model can quickly identify the type and extent of damage, enabling insurance adjusters to verify claims and estimate repair costs more accurately.

2. Automotive Repair Estimation: Car repair shops can use Car_Dent_Scratch_Detection(1) to provide accurate repair estimates to customers. By analyzing images of damaged vehicles, the model can identify the damaged areas and necessary repairs, allowing mechanics to more efficiently quote and plan the repairs needed.

3. Vehicle Auctions / Used Car Sales: The model can be integrated into vehicle auction platforms or used car sales websites to automatically assess the condition of each vehicle. By identifying and documenting the damage, the system can create comprehensive vehicle condition reports, ensuring more transparent transactions for both sellers and buyers.

4. Fleet Management: Companies managing a fleet of vehicles can use Car_Dent_Scratch_Detection(1) to monitor the condition of their vehicles regularly, identifying any damage or required maintenance. This allows for timely repairs, reducing downtime and ensuring the fleet remains in optimal condition.

5. Accident Reconstruction: Car_Dent_Scratch_Detection(1) can be employed by accident reconstruction experts to analyze images from the scene of an accident. By detecting and categorizing the damage, experts can better understand the sequence of events leading up to the accident and assist in determining the cause and liability.

Here are a few use cases for this project:

1. Underwater Infrastructure Maintenance: The model can help identify and classify underwater pipes for maintenance and repair activities, allowing professionals to easily assess the condition and plan necessary repairs for underwater pipelines and infrastructure.

2. Environmental Research and Monitoring: The model can be used for assessing the impact of underwater pipes on the surrounding ecosystems and water quality. This would help environmental researchers understand the potential risks of pipe leaks or spills, and develop contingency plans.

3. Marine Construction Planning: The model can assist engineers in industry or urban development projects by providing them with a clear understanding of existing underwater pipe networks, allowing for better planning and design in areas such as ports, offshore facilities, or coastal development.

4. Disaster Response and Recovery: In the event of natural disasters like hurricanes or tsunamis, the model can aid in identifying damaged or displaced pipes, helping emergency response teams to prioritize their efforts and make informed decisions for recovery and rebuilding.

5. Leak Detection and Monitoring: By identifying and classifying underwater pipes, the model can facilitate the monitoring of pipe health to detect potential leaks, leading to timely interventions to minimize environmental and financial impacts.

Overview

The Drowsiness dataset is a collection of images of a person in a vehicle (Ritesh Kanjee, of Augmented Startups) simulating "drowsy" and "awake" facial postures. This dataset can easily be used as a benchmark for a "driver alertness" or "driver safety" computer vision model.

Example Footage!

Training and Deployment

The Drowsiness model has been trained with Roboflow Train, and available for inference on the Dataset tab. We have also trained a YOLOR model for robust detection and tracking of a fatigued driver. You can learn more here: https://augmentedstartups.info/YOLOR-Get-Started

About Augmented Startups

We are at the forefront of Artificial Intelligence in computer vision. With over 94k subscribers on YouTube, we embark on fun and innovative projects in this field and create videos and courses so that everyone can be an expert in this field. Our vision is to create a world full of inventors that can turn their dreams into reality.

Smoke Detection Dataset

This computer vision smoke detection dataset contains images of synthsized smoke in both indoor and outdoor settings. Check out the source link below for more information on this dataset.

source:

Smoke100k dataset https://bigmms.github.io/cheng_gcce19_smoke100k/

Use Cases

• Identifying smoke indoors
• Identifying smoke outdoors (but not with aerial imagery)
• Identifying smoke-like object (eg: mist/steam from humidifiers)

Testing

You can test this model by using the Roboflow Inference Widget found above. The action hits the model inference API, which in turn produces the color coded bounding boxes on the objects the model was trained to detect, along with its labels, and confidence for each prediction. The feature also produces the JSON output provided by the API.