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Improving Object Detection with Deep Residual Learning

Wed Oct 06 2021
12m
Intermediate
Data Science

With a neural network-based approach to object detection, you can automatically extract the feature of an object from the image with help of a deep learning algorithm.

Improving Object Detection with Deep Residual Learning

Deep residual learning is a brand-new method of training deep artificial neural networks using the process of identity mapping for shortcut connections. This training method is also called ResNet.

But before we move on to using ResNet to improve object detection, we need to get some things out of the way first, especially since the very definition of ResNet we've given above contains terms that may just make people scratch their heads even more.

Let's Define Terms, Shall We?

Neural Networks

Neural networks, which are also referred to as simulated neural networks or artificial neural networks, are a subset of that branch of artificial intelligence known as machine learning. And since machine learning is the application of data and algorithms to imitate how humans learn with the ultimate goal of improving the accuracy of certain processes, it just makes sense that it would involve training neural networks, which reflect the human brain's behavior.

Artificial neural networks are comprised of a node of input layers, hidden layers, and output layers that connect artificial neurons and transmit data. And by reflecting human brain behavior, computer programs can recognize patterns and solve common AI, machine learning, and deep learning problems.

In conversations, neural networks and deep learning tend to be used interchangeably. And this can be confusing. You just need to understand that the word “deep” in deep learning simply refers to the depth of layers in the neural network. If a neural network only has two or three layers, it is merely a basic neural network. But if it consists of more than three layers, a neural network can already be considered a deep learning algorithm.

Identity Mapping

One other important aspect of deep residual learning is identity mapping or identity federation. It is the process of mapping identity in one realm to another identity in a different realm. This is when a security token transforms from one format to another, or when an identity from one realm joins together with an equivalent identity from another realm.

Using Deep Residual Learning for Object Detection

Object detection is a modern computer technology under deep learning. It deals with the detection of instances when semantic objects belonging to a certain class (such as buildings, cars, and humans) appear in videos and digital images. This technology is related to image processing and computer vision.

Object detection has been used in many different domains in computer technology, usually in computer vision tasks. These domains include pedestrian detection, activity recognition, and face detection. And it has applications in a variety of areas including video surveillance, image retrieval, autonomous driving, and healthcare.

Every object class carries its special features and attributes, and this approach helps computers classify objects according to class. This is the same kind of approach used in the development of face identification capabilities, where computers see features like eyes, nose, and lips, and detect other attributes like skin color, the proportion of features, and the distance between one feature to another.

Object detection involves the use of methods that generally fall under a neural network-based approach or non-neural approach. Of course, in this topic, we will only focus on the neural network-based technique.

Object Detection with Computer Vision and Deep Learning

Object detection is one of the most remarkable areas of computer vision and deep learning. This is because deep neural networks are known for their capacity to process visual data. And they've become a core component of a significant number of computer vision applications in recent years. The key problem that neural networks can help solve is the detection and localization of objects in images.

With the neural network-based approach to object detection, you can automatically extract the feature of an object from the image with help of a deep learning algorithm. Deep learning algorithms include Convolution Neural Network, Variance Auto-Encoder, and Auto-Encoder, among others.

With machine learning, object detection allows a computer program to detect objects in an image by first extracting its features and then classifying these objects based on these features and attributes. However, taking it one step further and applying the deep learning-based object detection method, your computer can identify an object in an image by looking into that object's learned features through a Convolutional Neural Network.

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