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3 posts tagged with "Computer Vision"

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· 3 min read

yolo-algorithm-and-its-Applications-in-computer-vision YOLO Algorithm and its Applications in Computer Vision

What is the YOLO Algorithm?

The YOLO algorithm is a computer vision technique that allows us to detect objects in images and videos. This algorithm is different from other object detection algorithms because it can identify multiple objects in an image or video frame. It was originally developed for human detection but has been extended to other tasks such as vehicle detection, face detection and pose estimation.

How does the YOLO algorithm work?

The YOLO algorithm is a computer vision technique that detects objects in images and videos. It was developed by Joseph Redmon and Ali Farhadi. The algorithm is based on the principle of "you only look once" (YOLO), meaning that it can detect objects in an image or video with high accuracy and speed.

Applications of YOLO models in Computer Vision

There are many potential applications for YOLO models in computer vision. Some of the most promising applications include object detection and tracking, image classification, and activity recognition.


Object detection and tracking is a key application area for YOLO models. This technology can be used to create systems that can automatically detect and track objects in images or video. This could have a wide range of applications, from security and surveillance to automotive safety.


Image classification is another significant application for YOLO models. This technology can be used to automatically classify images into different categories. This could be used for tasks such as image search or content moderation.

Activity recognition is another important application for YOLO models. By detecting humans and their movement patterns, computers can be trained to recognize various activities, such as walking, running, and jumping. This information can be used for a variety of purposes, such as improving sports training programs or reducing the likelihood of accidents in public spaces.


Human pose estimation is another interesting application for YOLO models. Using these models, it is possible to estimate the 3D positions of people in images or video. This information can be used for a variety of purposes, such as virtual reality or augmented reality applications.

There are many other potential applications for YOLO models in computer vision. These are just a few of the most promising applications for this powerful technology.

Yoga-image is a platform which enables developers to create their own Computer Vision Artificial Intelligence models without writing a single line of code. Train and deploy your own Computer Vision models within minutes. What's more? Developers will also be able to monetize their Computer Vision models, they will also be able to generate synthetic image datasets to be used for training the models on

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· 4 min read


Ever wondered how smart our eyes and brain are? What if we could train a machine to become smart to a certain extent? For example, we can look at images of skin and figure out if there’s some disease. We could train a machine to look at images and classify them into different classes positive (skin has some disease) and negative (skin does not have a disease).

So why don’t we use this approach of classifying images in a machine? Is it possible for machines to sense how a human can sense it? I know there are plenty of questions running through your mind, especially for people who are Beginners and not very acquainted with the world of AI and computer vision. is a platform that allows developers to build their own computer vision model without writing a single line of code. This platform takes you through a few steps and then at the end, hurray…!! It's done. Now the machine can detect between skin infected with a disease and healthy skin. Yes, we know what`s running in your mind again. It is “How?” How are the classification and detection done? This is done by training the model with datasets of relevant images so that the machine can learn and predict the answer by looking at an image. To know how this can be done in more detail, please visit

Right today we will take some examples of applications wherein the models are mostly used in Drones. Some of the image classification models useful for drones have been built using the platform by our team and also by fellow AI enthusiasts who have joined us in our monthly webinars.

Computer Vision applications of Drones:


Drone service providers and organizations using drones see a direct benefit of using Computer Vision to reduce the processing time of the video feed, at times making it so fast that the objects can be detected from the drone feed in real time using computer vision without any manual intervention. We welcome all drone service providers and organizations using drones to use to build their own classification and detection computer vision models which can be integrated with their applications.

Using Image Classification Models for processing the video feed captured using Drones:

Image classification is the process where the specific data points in the image are predicted or classified. The main aim is to identify all the data points from an image.

1. Windmills:

  • Computer Vision models can classify cracks on the rotor blades and the rotors.
  • Computer Vision models can classify the rust formed on the rotor blades and the rotors.

2. Surveillance, Intrusion detection:

  • Farmers living on the edges of forests can use animal classification models to know when and what type of animals are entering their premises.


3. Geographic mapping:

  • Many governments are using computer vision to classify the severity of potholes depending on the depth of the potholes.

Using Image Detection Models for processing the video feed captured using Drones:

Image detection or Object detection uses computer vision technology to detect the objects in the image.

1. Object detection and counting:

  • Drones are used in agriculture to detect and count livestock, trees, fruits, and vegetables. Computer Vision models can help automate these tasks.
  • Fire Detection: Forest fires are a big threat to the environment, animals, and humans. CCTV cameras and drones can be used to detect fires in open areas where sensors cannot be used.
  • Crack, rust, rooftop damage detection

2. Geographic mapping:

  • A detection model can be used to detect potholes on the roads. A classification model further helps to classify the severity of potholes. pothole
  • Counting the number of trees in farms and estimating the economic value of the produce.
  • Flood control Management - the aerial detection of floods.
  • Land surveyors use computer vision models along with video analytics to automatically classify and count the number of houses, buildings, farms, structures, trees, etc in a given area to understand land usage patterns and to help the government find illegally constructed houses, to help the town planning authorities to plan road development and provide amenities to the citizens. geographical-mapping

3. Law Enforcement:

  • Search and rescue: Detecting people and animals using drones.
  • Number plate detection is mostly seen on highways. An OCR model can help them in reading the letters and numbers on the license plate of the vehicles. number-plate

4. Wildlife monitoring

  • Counting animals.
    • Example: Saaragh has developed a kangaroo detection model.

You can register your interest in this form to get early access to to build your own computer vision AI models without writing a single line of code:

· 4 min read


Challenges in AI development

Artificial Intelligence market size is growing and it is said that it can grow up to $15.7 trillion by 2030, as quoted in the research paper

As AI grows, the impact and challenges rise parallely as well. Let's see some of the most common challenges in Artificial Intelligence Development. has figured out six challenges for developing Artificial Intelligence models:

1. Research: Wondering why “research”? I am sure that most of the articles might not add this as a challenge, but the first thing every developer does when they have a problem is that they do research.

  • Research can be for Datasets.
  • Research can be done on what are the available solutions/ ways to solve the problem.
  • Research can be on the code.
  • Research can be done
    • By reading Blogs and articles.
    • By watching Vlogs.
    • By studying research papers.
    • By browsing multiple websites and forums.

2. Dataset Preparation: We know that the output AI model is completely dependent on the data which we use to train the model, so dataset preparation is very challenging for every company.

  • Dataset requirement understanding-
    • finding the right dataset for your problem and understanding the data.
  • Collection of data-
    • Getting the data which is collected from a number of sources and millions of users so there might be some sensitive data which leads to data breach.
  • Getting good quality data requires a lot of time to find and label the trained data, preprocess the images, and annotate the images.
  • We have to store large and comprehensive data in data storage and we need to version it when the new data gets updated.

3. Code Development:

  • The big challenge for everyone here is writing the code for the AI model.
  • Complex libraries are involved while developing ML code.
  • Storing the code and code versioning .

4. ML Model Training:

  • Finding efficient Model
    • What model is the most efficient for the user? We may not be knowing all the models. We should know a few parameters of the models.
  • Understanding algorithm
    • Need to understand the algorithm, model, libraries.
  • Parameters to be set during model training
  • When the model is trained, how the model is validated.
  • Improving model and fine tuning.
  • Versioning the model and storing the trained model.

5. ML Model Testing:

  • Testing trained models either manually or automatically and both have their own challenges. -Automated testing involves a lot of code.
  • Analysing and visualising metrics.
  • Storing test results history logging.
  • Comparing model versions.

6. ML Deployment:

  • Complex libraries are used in ML deployment and setting up servers to support those libraries.
  • Model serving and handling changes from model to model.
  • Libraries installation and managing versions.
  • API development and integration of the models with applications.
  • Using technologies like Docker application to avoid portability and cross platform integration.


Build Your Own Model Without Writing a Single Line of Code using


  • The no-code platform for now is mainly focusing on Computer Vision problems.
  • Will provide examples to help you understand which model will be relevant for your application.
    • Example: you can use to build an image classification model using the datasets of different types of leaves - leaves with diseases and leaves without diseases.
  • Easy to add/update/delete custom data
  • One click model training is available.
  • The trained models are stored and can be accessed anytime.
  • The trained model is validated and tested in the same platform.
  • Multiple export options are available for deployments and automatic integrations.

View the recording of our webinar where we discussed these challenges in AI model development and the solution: Image alt text

Ready to create your own computer vision model without writing a single line of code? Request access to NOW by filling up this form: