Browse » Damage & Risk Assessment

Top Damage & Risk Assessment Datasets

Optimize operations by deploying computer vision for asset inspections, increasing engineer productivity, and promoting safety. Use cases in Damage & Risk Assessment include, but are not limited to: structural damage assessment, leak notifications, surveying for debris and damage, monitoring pressure gauges, roof damage inspection, monitoring and locating fire hazards, promoting fire or accident safety, and creating apps for damage assessment on insurance claims.

Example Project for Damage Assessment: https://blog.roboflow.com/computer-vision-assisted-structural-damage-inspection-using-drones/

crack
8

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.

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

Inferred Image from YOLOv7

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.

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.

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

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!

Distracted Driver 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.

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 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 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 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

gauge
2

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

cables
3

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