Object Recognition with AI: How Machine Learning and Deep Learning Work Together

Vishaal

August 9, 2023

Flashback to the year 2018, Apple drops the fingerprint scanner to launch iPhones with Face ID. Although the face scanning feature was present on Android for many years, Apple reinvented it by adding depth recognition with increased precision. We have come a long way now with numerous updates and modifications to perfect it. But have you wondered how your smartphone can recognize your face, even in pitch dark? How they do object recognition?

If you are still using Facebook or if you remember the platform, you’d definitely know about the AI-based image recognition process for the tag option in posts. You also might know about the Tesla cars that people drive on auto-pilot mode seamlessly avoiding obstacles and abiding by all the rules. If you still wonder how these things are done, it is possible with a technology known as object recognition.

What is Object Recognition?

The computer vision technique that enables machines to identify and locate objects in videos or images is known as object recognition. This technique is being implemented in a wide range of domains such as video surveillance, autonomous driving, etc., and its use cases are endless.

Before diving into the techniques involved in computer vision, we will take a look at the commonly used terms to understand it.

Image classification:

Image classification is a process that involves assigning a class label to an image. It can be helpful in identifying the object in an image such as a dog or cat. However, this technique can only produce results that would assign a class label to the object. It will not give any information about the location or the number of objects as it will automatically assume that only one primary object of interest is present in it.

Object localization:

This involves drawing a bounding box around one or more objects in an image. By doing so, it will assign a class label to each bounding box.

It will provide information about the location and the size of the object present in the bounding box. It can also distinguish between different classes. On the downside, it cannot trace their exact shape or boundaries, it just draws a bounding box around the object to contain it.

Object detection:

Object detection will combine both techniques and will be able to trace their location inside the image with higher accuracy. It can also provide information on the location, size, and class of each object in an image. For instance, when you upload an image of two dogs and one cat, it will properly label them as dog 1, dog 2, and cat with a proper bounding box around them.

However, object recognition does not end here. It comes with several other challenges such as:

• Dealing with variations in object appearance which can be deformation, illumination, unusual poses, blurred edges, etc.
• It will struggle while handling large-scale datasets as it will have complex scenes and objects with a wide diversity.
• It is not easy to achieve high accuracy and efficiency in real-time applications.

What is object recognition in machine learning?

Machine learning is another branch of AI that empowers computers to learn from data and to make predictions/decisions upon them. Here are some basic terms that you should know to get started as we are already using machine learning in daily life unconsciously –

Supervised learning:

It is a classification in machine learning where the data is labeled with the desired output. So it is basically feeding the necessary data to the machine. Just as the name suggests, the machine will be fed with a set of examples by the supervisor to train them in order to produce the desired outcome.

Unsupervised learning:

On the contrary, unsupervised learning is training the machine using unclassified information and letting the algorithm act without guidance. Since there will be no supervisor involved, the machine will have no idea about the features of objects in the image. On the brighter side, it will allow the model to discover its own patterns and information which might be new to us.

What is deep learning?

Deep learning is a method in AI that teaches computers to process data in a way by communicating with each other. Deep learning is different from machine learning in several ways even though it uses artificial neural networks to learn from data and make predictions or decisions.

It stands apart from machine learning in the following aspects:

Architecture:

Deep learning models have multiple layers of neurons which is helpful in learning complex and abstract features that are present in the data. On the other hand, machine learning models have simple and linear structures that mostly rely on custom-made features based on the algorithm.

Optimization:

Machine learning models use several methods like quadratic programming or genetic algorithms to optimize their parameters and minimize the loss function. On the contrary, Deep learning models typically use gradient-based methods like SGD to get it done.

What is artificial neural network?

It is basically the building block of deep learning and was named after the human brain for its similar functionality as the billions of neurons communicate with each other to get the tasks done.

Artificial neural networks are of three types –

• Data inputs
• Weight and bias
• Activation functions

Furthermore, there are different types of neural networks which could be useful in different applications and tasks. Here are some of the common types of neural networks –

• Convolutional neural networks
• Recurrent neural networks
• Fully connected layers

What is object recognition?

Object recognition is a cool concept that involves finding and naming objects in images or videos. It can be anything from a busy street scene to a picturesque landscape. The objects will be located and recognized by drawing boxes around them. It can be helpful in many domains such as –

• Social media
• E-commerce
• Healthcare
• Entertainment
• Education
• Security

So where do machine learning and deep learning fit into object recognition?

They both have their set of pros and cons which makes it difficult to rely upon in all scenarios. Machine learning methods are known to be faster but it often misses out on important details such as the structure of the objects. These methods also require a lot of data and effort to make effective predictions.

On the other hand, object recognition with deep learning might achieve high performance on tasks but they need more computational resources and data to refer and infer. If the network architecture or hyperparameter is not well chosen, it might lead to overfitting and underfitting problems.

Needless to say, they both are not mutually exclusive as they are often combined in many use cases. For example, you can use machine learning to generate region proposals or candidates for object detection. Then, you can deploy deep learning to classify them properly. They both are necessary for object recognition and complement each other in their ways and nays.

Conclusion

AI object recognition is a fascinating field in computer science that both humans and machines can learn. Despite being used in many use cases, it is still being researched by experts and is expected to play a key role in the future of computer vision. The future of AI object recognition is bright, and we can expect to see even more innovative and impactful applications in the years to come.

The future of AI object recognition is bright, and we can expect to see even more innovative and impactful applications in the years to come. Now that we have Generative AI that recognizes and creates objects and people based on prompts, do you think humankind is ready to move on to a new future?

Vishaal

09 Aug 2023

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