Object Detection

Automated Computer Vision Inspection of Physical Pipelines

In this guide, we show how to identify various types of pipeline defects using computer vision.

How to Train YOLO-NAS on a Custom Dataset

YOLO-NAS is the latest state-of-the-art real-time object detection model. Learn how to train YOLO-NAS on your custom data.

Zero-Shot Image Annotation with Grounding DINO and SAM - A Notebook Tutorial

In this comprehensive tutorial, discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model. Learn how to convert object detection datasets into instance segmentation datasets, and use these models to automatically annotate your images.

Synthetic Data Generation with NVIDIA and Roboflow

Learn how to build computer vision models that leverage synthetic data using NVIDIA Omniverse and Roboflow.

Grounding DINO : SOTA Zero-Shot Object Detection

Most object detection models are trained to identify a narrow predetermined collection of classes. Zero-shot detectors like Grounding DINO want to break this status quo by making it possible to detect new objects without re-training a model.

How to Code Non-Maximum Suppression (NMS) in Plain NumPy

Double Detection in Computer Vision If you’ve been working with object detection long enough, you’ve undoubtedly encountered the problem of double detection. For some reason, the model detects

Building a Computer Vision Assisted Pill Inspection System

The article below was contributed by Timothy Malche, an assistant professor in the Department of Computer Applications at Manipal University Jaipur. Pill Inspection System Overview This project creates a system

Track and Count Objects Using YOLOv8

Counting moving objects is one of the most popular use cases in computer vision. It is used, among other things, in traffic analysis and as part of the automation of manufacturing processes. That is why understanding how to do it well is crucial for any CV engineer.

How to Draw a Bounding Box for Computer Vision with Python

In this post, we discuss how to use the cv2 library to draw and fill a bounding box in Python.

Computer Vision Assisted Structural Damage Inspection Using Drones

In this post, Timothy Malche walks through how to inspect structural damage with computer vision and drones.

Launch: Roboflow Integration with Ultralytics HUB

Ultralytics, the creators of YOLOv5, and Roboflow now support an integration making it easier to import YOLOv5 models from HUB to Roboflow, export datasets to Ultralytics HUB from Roboflow, and

📸 Roboflow 100: A Multi-Domain Object Detection Benchmark

Roboflow 100 (RF100) is a crowdsourced object detection benchmark. The dataset consists of 100 datasets, 7 imagery domains, 224,714 images, and 829 class labels with over 11,170 labeling hours.

Using Computer Vision to Save Sea Lions

What is causing the sea lion population to decrease? Is it illegal hunting? Is it shark and killer whale predation? Or maybe it’s overfishing, causing the sea lions to

Top 6 Gaming Datasets for Computer Vision Projects

Today's article will show you the top 6 gaming datasets from Roboflow Universe [https://universe.roboflow.com/browse/gaming] to help provide inspiration for using video games or

School Bus Detection Using YOLOv5 (Tutorial – Part 2)

This is a guest post by Kristen Kehrer [https://www.linkedin.com/in/kristen-kehrer-datamovesme/https://www.linkedin.com/in/kristen-kehrer-datamovesme/] , Developer Advocate at CometML [https://www.comet.com/site/].  Since

What is Object Tracking in Computer Vision?

Tracking the movement of an object has many applications, from tracking robots in a warehouse to implementing object tracking systems in drones. The basics of object tracking [https://blog.roboflow.

Top 6 Manufacturing Datasets for Computer Vision

Manufacturing is an industry that has found many successful use cases and applications for computer vision. Vision AI helps avoid increases worker safety, decreases human error, and saves time automating

Launch: Smart Polygon Labeling

Roboflow Annotate [https://roboflow.com/annotate] now offers automated polygon labeling for all users. With as few as one click, you can apply a polygon annotation to objects in your

Building Custom Computer Vision Models with NVIDIA TAO Toolkit and Roboflow

NVIDIA's TAO Toolkit provides a framework for fine-tuning popular computer vision models using your own data. In this tutorial, we'll be demonstrating how to use Roboflow to curate a high-quality computer vision dataset to use with NVIDIA's TAO Toolkit.

Using Object Detection to Trigger Automated Email Alerts

In this tutorial, we'll show you how to use object detection to identify specific configurations within an image to trigger email notifications. This setup demonstrates how you can

Use Raspberry Pi and Luxonis OAK to Deploy Vision Models in Robotics

Computer vision can enable robots to intelligently adapt to dynamic environments. With Roboflow [https://roboflow.com/] and a Luxonis OAK [https://www.luxonis.com/], you can develop and run powerful

How to Deploy YOLOv7 to a Jetson Nano

We'll be creating a dataset, training a YOLOv7 computer vision model, and deploying it to a Jetson Nano to perform real-time object detection.

How to Use Tiling During Inference

Tiling is especially helpful and can improve accuracy for aerial images and small object detection. Like the human eye, computer vision models can have a difficult time detecting small objects

Using Polygon Annotations for Object Detection in Computer Vision

Benefits to Existing Models Polygons have traditionally been used for training image segmentation models [https://blog.roboflow.com/instance-segmentation-training-roboflow/], but polygons can also improve the training of object detection models

How to Use Polygon Annotation and Labeling with Roboflow

Polygons have traditionally been used for training image segmentation models, but they can also improve the training of object detection models. Object detection models are typically much faster and more widely supported, so they're still the best choice for solving many problems.