This dataset was originally created by Jhonathann. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/cone/capacetes-e-cones.

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 Justin Henke and Reginald Viray. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/psi-dhxqe/psi-rossville-pano.

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 王天裔 (Wang Tianyi). To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/project-sign-detection/traffic-sign-cdfml.

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

Bus_YOLOv5 is a bus and truck object detection dataset with 9,900 images, and a trained model.

Use the Bus_YOLOv5 dataset and detection API in computer vision applications for transportation, self-driving cars, safety and security, and more. Track busses and trucks using this Bus_YOLOv5 model, or use your home security camera to create notifications for delivery trucks or city busses.

Check out these posts from the Roboflow Blog for inspiration:

• How to Implement Object Tracking
• Using Object Detection to Trigger Automated Email Alerts

This dataset was originally created by Yudha Bhakti Nugraha and Kris. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/7-class/11-11-2021-09.41.

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 Muntaser Al Abdulla Aljouma. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/due/detection-dlzhy.

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 project labels container codes on trucks. It can be used with optical character recognition (OCR) software to identify vehicles entering and exiting facilities or passing a checkpoint via a security camera feed or traffic cam.

The project includes several exported versions, and fine-tuned models that can be used in the cloud or on an edge device.

This project was created by downloading the GTSDB German Traffic Sign Detection Benchmark

dataset from Kaggle and importing the annotated training set files (images and annotation files)

to Roboflow.

https://www.kaggle.com/datasets/safabouguezzi/german-traffic-sign-detection-benchmark-gtsdb

• Original home of the dataset: https://benchmark.ini.rub.de/?section=gtsdb&subsection=dataset - Institut Für Neuroinformatik

The annotation files were adjusted to conform to the YOLO Keras TXT format prior to upload, as the original format did not include a label map file.

v1 contains the original imported images, without augmentations. This is the version to download and import to your own project if you'd like to add your own augmentations.

v2 contains an augmented version of the dataset, with annotations. This version of the project was trained with Roboflow's "FAST" model.

v3 contains an augmented version of the dataset, with annotations. This version of the project was trained with Roboflow's "ACCURATE" model.

• Choosing Between Computer Vision Model Sizes | New and Improved Roboflow Train

A dataset and fine-tuned model for recognizing identifiers on container trucks. Combine with an OCR (optical character recognition) package to ID vehicles passing a checkpoint via a security camera feed or traffic cam.

The project includes several exported versions, and a fine-tuned model that can be used in the cloud or on an edge device.

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.

High School Introduction to Robotics: Drone Machine Vision Project We train, validate, and test our algorithim using images from the testset-dev set of Task 1: Object Detection in Images, from VisDrone-Dataset. For some of these 546 images, we manually clasified objects as houses (any building, including homes, appartments, etc) and cars (any motor vehicle, including cars, trucks, etc). We attach an iPad Mini to our drone, then use the Photo Timer+ app to take 50 pictures every 5 seconds as the drone is flying. Then, we import those pictures to this RoboFlow project to see if the machine vision algorithim is able to identify and classify houses and cars from these images.

Here are a few use cases for this project:

1. Traffic Management: Use the "Vehicle Detection" model to monitor and manage traffic flow on roads and highways in real time. By identifying different types of vehicles, traffic control centers can optimize traffic light timings, detect traffic congestion, and implement measures for better road usage.

2. Parking Surveillance and Management: Implement the model within parking management systems to identify different types of vehicles, ensuring optimized allocation of parking spaces. A separate section for vans, trucks, and trailers could be maintained, enhancing the parking experience.

3. Autonomous Vehicle Development: Use the model in the development of autonomous cars. Being able to differentiate between different types of vehicles on the road is crucial for safe and efficient autonomous driving.

4. Security and Surveillance: Employ the model in video surveillance systems to identify any suspicious vehicles like unattended trucks, vans or buses in prohibited areas. It can help detect unusual activities or threats.

5. Intelligent Transport Systems (ITS): Implement the model in ITS to offer differentiated services. This model can help in predicting travel times, enforcing rules for different vehicle classes, planning improved public transportation systems, etc.

Dataset description

This data set was builded with the aim to develop an automatic people counter for the public transport system buses in the city of Santiago de Chile. The model must be loaded on an embedded device like the OAK-D plattform and the principal goal is try to characterize the passenger flux on the bus route. For that we mount three cameras inside the machine to count the number of people that onboards and alight in diferentes places during the journey, besides of estimate the number of passangers on the road and them who are waiting in the bus stops . This last thing could be accomplished using a fourth camera outside the bus.The images used are extracted from a videorecord series obtained during a sequence of jurneys. By this way we can know th estimated proprtion uf passsanger that uses certain type uf bus for travelling trough the city.

Use cases

In the past we have used the MobileNet network for trainning and detcetion by SSD detector. In that case we used an lateral / inclined view. In this opportunity we are using a topview to recognize the heads or/and shoulder using an Hough detector like algorithm. Also we are studyng the posibbility to use the YOLO detector on a TensorFlow framework.For tracking the trajectory of the people we are using DeepSort and LKT optical flow trackers.

About Our Team

We called ourselves the CHELKATUN NAUKA TEAM . This words are a fusion between the Chelkatun word thata means "Sharing", whom origin cames from the ancestral languague known as mapudungün and the polish word NAUKA whose meaning is "Knowledge" . This project its an international collaorations bettween post graduated students in computational sciences from Spain and Belgium and a student from Chile that is working actually on this project for his Physics Engineerng Thesis Project.

Also we are participating on the 2021 OPENCV AI SPATIAL COMPETITION.

If you want more information about us , please feel free to contact us by this email adress : sebastian.fuentes.p@usach.cl to Sebastian, the student from Chile.

Here are a few use cases for this project:

1. Maritime Traffic Management: The ShipClassification model can be used by maritime traffic control centers to automatically identify and monitor ship activity in their vicinity, making it easier to manage traffic and ensure safety in high-density ship lanes.

2. Military Surveillance: Armed forces can use the software to identify approaching vessels and verify their intentions, being particularly useful in identifying military ships. It can also be used to analyze satellites or drone imagery for larger geographical survey areas.

3. Port Operations: Port authorities can utilize the model to identify incoming and outgoing cargo container ships, tankers, and RORO vessels to facilitate better logistics management, scheduling, and time efficiency in docking procedures.

4. Environmental Conservation: Marine conservation organizations could use the ShipClassification model to monitor and identify tanker activities in sensitive environmental areas, enabling them to detect and respond to potential threats like oil spills more quickly.

5. Travel and Hospitality Industry: Cruise companies and tourism agencies can employ the model to detect and track their cruise ships for real-time updates and safety purposes. It can also be used to create engaging experiences for customers by informing them about the various types of ships they may encounter during their cruise journey.

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.

From our 2021 Annual Report

Mission

HelicoptersofDC is dedicated to bringing DC residents the most accurate and up-to-date information on what's overhead and to foster collaboration and education about helicopter identification and mission profiles. 2020's GAO report found that 96.3% of helicopter noise complaints to the MWAA from 2018-2021 were unattributed to any specific operator - this is why we feel it's important to educate the community on helicopter identification in addition to reporting what is happening currently and why.

I'd like to thank our Patreon supporters, their contributions cover server costs and allow me to invest in hardware to bring radio calls to the platform.

Unique Strengths of the CopterSpotter System

Many flight tracking sites like Flight Aware, Plane Finder and Radar Box remove flights at the owner's request. Even ADSBExchange, which doesn't filter any flights, does not receive tracking information from most military helicopters due to the Code of Federal Regulations Title 14, Chapter I, Subchapter F rule 91.225 which makes an exemption to mandatory flight transponders for "sensitive government mission for national defense, homeland security, intelligence or law enforcement."

This makes an enormous amount of helicopter flights over the NCR untraceable in any systematic way. As a result, we rely on user submitted photos and identification of helicopters in addition to a separate system that allows users to categorize helicopter radio calls to/from DCA tower.

This dataset is sourced from user-submitted photos on Twitter, and powers a computer vision program to automatically infer submissions to @HelicoptersofDC so DC residents can better identify what's over their heads.

Helicopters of DC (@HelicoptersofDC)

.@fergindc spotted an H60 and has 1997 points 🔭🎷 #CopterSpotter Probable operators: Army/National Guard/FBI/Possibly Presidential twitter.com/a/status/15570… pic.twitter.com/YOmY5VTxoX

Tweet link

Operators around the NCR can be reasonably inferred by aircraft type:

• Air Force - UH1(N/H)
• Army/Navy/Nat'l Guard - UH60 / H60 / UH72(A) / SH60(m)
• Coast Guard - MH65 / AS65
• POTUS - V22 / VH60(N) / VH3D / VH92(A)
• Medical - EC35 / EC45 / A109(e) / EC135 / EC145
• DC/MD/VA Police - AS350 / AS50 / MD52 / A139 / B429
• Park Police - B412 / B206

We have identified the following exceptions:

• AS50/AS350 is used both by DC Police and at least 3 different DHS helicopters that frequent the area
• B412 type is included in Federal Law Enforcement and Emergency support, most frequently Park Police but can also be Dept of Energy's 'Nuke Sniffer' helicopter
• B06 is included in Federal Law Enforcement here as Park Police #N33PP. However, it can also be @Chopper4Brad's news helicopter.
• B429 included in State and Local for Fairfax County Police, similarly H60 used in Military, but both crafts are used less often by FBI

Future considerations:

This dataset includes classes for three variations of UH60 Black Hawk: H60, VH60N, and H60 + Aux Fuel Tanks. We may merge this later, the initial thought process was the aux fuel tanks change the shape, and the distinct color of the VH60N (when photographed with front light) can be attributed uniquely to Marine Presidential Squadron unlike the rest which can be Army, National Guard, FBI or Customs and Border Patrol.

Release Notes:

• August 19th, 2022 1.3 - Adjusted rotation to +/- 5º, retrained model from scratch

• August 5th, 2022 1.2 - Now 12,000+ images, added many photos of previously under-represented classes R66, VH92A, S76

• Added CH47 class, added bird class, added plane class, added null examples

• Rotation: Between -10° and +10°

• May 6th, 2022 1.1 - Doubled images to 4061, specifically adding previously under-represented classes such as R66, V22 and A139 - added image augmentations:

  • Flip: Horizontal
  • Rotation: Between -2° and +2°
  • Saturation: Between -50% and +50%
  • Exposure: Between -25% and +25%

• Trained on Accuracy as opposed to Speed: https://blog.roboflow.com/computer-vision-model-tradeoff/

• 1.0 - the test version comprised of over 2000 images (that project is now deprecated)

Here are a few use cases for this project:

1. Autonomous Vehicles Navigation: This model could be vital in recognizing obstacles for self-driving two-wheeler vehicles. These vehicles could use the model to identify potential road hazards and navigate around them safely.

2. Road Maintenance and Safety: Local municipalities or city planning teams could use this model to proactively identify and repair road damage like potholes, subsidence, or loose surfaces, thus improving road safety.

3. Augmented Reality Touring: This model can be used in AR-based touring applications to provide cyclists or motorbike riders with real-time updates about upcoming obstacles or potential hazards on their route.

4. Game Development: Developers of driving or biking simulation games can use this model to make their virtual environments more realistic by including a diverse array of street obstacles that players must avoid.

5. Insurance claim processing: Insurance companies could utilize this system to assess the validity of claims related to two-wheeler accidents. The system could analyze video data to determine if reported obstacles were indeed present and had contributed to the accident.

This is an object detection dataset that me and my teamates have created. We picked these images from Google Earth. These images are aerial images in which we will be detetcting objects according to cateogary. The images consists of different cateogaries such as Airplane, Bridges, Playground, Water-Bodies and Vehicles. The Dataset consists of total 700 images out of which 490 are used for training ,140 for validation and remaining 70 for testing. We have first uploaded these images and then annoteted them on roboflow itself. The version used for this project is Earth Vision small scale dataset version4.

Создание ИИ по определению автомобилей

Разбиение на кадры: ffmpeg Архитектура: YOLOv5 На будущее о YOLOv5: https://youtu.be/MdF6x6ZmLAY Видео о roboflow: https://www.youtube.com/watch?v=VDqsK3FDIsQ Размер изображений: 100x100

аннотаций 1 тип - vehicles - это все ТС абсолютно

По этому не уверен *Все аннотации идут в training set, после завершения выделения и нажатия на кнопку Add images to Dataset выбираем Add all images to training set . *

HRSC 2016

Source:

This is the dataset named "HRSC2016". This dataset is collected and processed by other researchers, but comes without annotations. This project is to annotate the ships in this dataset correctly, with polygon masks. This will be used for semetic segmentation tasks later.

Currently the annotation is still on-going.