Object Recognition

Developing an Object Recognition software sounds like a tedious process but it has multiple use case for multiple industries as we are giving the power to machines to see through – Lean – Derive results. This is made possible with the use of machine learning and deep learning over the data collected by Object recognition.

Multiple Tech giants have already jumped into this interesting and result oriented sector for optimizing revenue and time across multiple real life sectors.

Object Recognition

Difference between Object detection and Object Recognition?

Object Detection

In object detection, the computer vision algorithm will be able to find an object present in an image or a real time video. This also includes for the algorithm to deduct the reoccurrence of the object frequency in the same image.

Object detection algorithm finds that it’s a Bird and presence frequency is once in the image.

Object Recognition

In Object Recognition, the computer vision algorithm will be able to find specifically identity what is in that image or real time video just like how human brain concludes solutions on seeing the image.

Recognition algorithm finds the type of bird flying in the image.

How does object Recognition work?

There are two main commonly used methods for object Recognition: Machine learning and Deep learning. Both the methods execution process for identifying is different. Lets take a more closer look below:

Deep Learning

CNN in Deep learning stands for Convolutional Neural Networks are used to learn the unique features present in an image to identify the subject. Take for an example, CNN can identify differences between Humans and Dogs. By training with multiple data, CNN will be able to recognize that Humans are different and dogs are different. It will be able to conclude the presence and frequency of it in the image or video.

Methods for object deduction using Deep learning

  • Scratch level Training model

This will be a tedious process as you need a large amount of data of the subject. You will have to set up weights & layers in the Convolutional Neural Networks (CNN). The results can vary on the amount of data used to train the model.

  • Using pre-trained model

There multiple pre-trained models out there in there in Github which are open source. You can use this pre-trained model to fasten your work and concentrate on the other part. But again, the accuracy of the object recognition depends on the trained model used.

Machine Learning

Machine learning is the widely used approach taken for Object Recognition. There are a variety of existing machine learning algorithm for object recognition. To name a few – Viola-Jones algorithm, HOG – extraction method are the most famous ones. These algorithms can also be used to identify faces.

With machine learning, the object recognition process becomes easier compared to deep learning due to its flexibility of learning with a little lower data set comparatively.

To conclude, if you have faster GPU and Lots of Data, you can go with Deep learning for object Recognition instead of Machine Learning. Deep learning requires a high spec GPU processing for its model training. Comparatively, for smaller projects Machine learning is recommended. But also note, Deep learning has more accuracy level compared to Machine learning on Object recognition software development.

Our work with Machine learning and Object recognition

At Sparkout Tech, we are into Object recognition and Object deduction software development to derive results in an enterprise application. Below is a demo application developed by our ML-AI team on Tensor flow, React Native for Object Recognition. Our Team is constantly working on increasing the efficiency of the model which we have trained. Take a look at the video below:

Industries where
Object Deduction can be used

  • Security
  • Manufacturing
  • Healthcare
  • Retail
  • Social Media
  • Automobile
industries

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