Deep learning is one of the subfields of Machine Learning (ML). This particular subfield of Machine Learning is gaining immense power with time. Deep learning has solutions for or can solve highly specific requirements. Therefore, it has the potential to change the future. This article will discuss how Deep learning can extend and change the future of Machine Vision forever.
What are Deep Learning and Machine Vision?
Deep Learning is a subfield of Machine Learning, which itself is a sub-part of Artificial Intelligence (AI). The algorithm of Deep Learning has been coded in such a manner that it exhibits certain functions identical to the human brain. Being a vital part of data science, Deep learning consists of a prophetic and statistics framework.
Machine Vision (MV) is a type of technology that allows a computer to see. One or more cameras assist the computer system. These cameras capture information as well as images and transfer the data to a robotic controller. Machine Vision needs the assistance of Artificial Intelligence and Deep Learning to stimulate image processing.
How Deep Learning is beneficial for Machine Vision
Deep learning is considered to be an essential technology in the arena of data classification. Earlier it was not possible to increase several applications in Machine Vision (MV) but the upgradation of Deep Learning has made it practical. Moreover, with the help of Machine Vision, the performance of the applications can also be enriched. Deep learning has generated new opportunities in numerous sectors like agriculture, logistics, pharmaceuticals, and others.
The operating techniques of Computer Vision with the assistance of Deep Learning
Computer Vision or Machine Vision requires lights, lenses, cameras, and picture sensors to function and capture images appropriately. The demand for Machine Vision has rapidly grown in the industries to ease and handle industrial obstacles. With the help of Deep Learning the entire Machine Vision System can perform accurately and efficiently. Deep learning can automate this entire system. Computer Vision also uses Convolution neural networks (CNN). This is a multi-layered deep-learning model. CNN can accurately classify and identify pictures. Therefore, can extract particular elements from a given photograph.
The required architecture of Deep Learning for Machine Vision
The architecture of a CNN determines its efficiency as well as performance. The architecture of a CNN consists of numerous structures that exist in various layers. The design and setting of various elements are essential for an effective CNN. There are many effective CNN designs like AlexNet, VGGNet, and others. These designs utilize several convolutional layers, connected layers, and GPU.
Application of Deep Learning in Computer Vision
Deep Learning is used to improve object detection. There are two kinds of object detection:
- One-Step object detection - this was invented to fulfil the requirement of real-time detection. In this method detection and classifications are combined with bounding boxes. This makes the process easier and faster.
- Two-Step object detection- this object detection is quite slow as the first step needs a Region Proposal Network (RPN). And then in the second step, an RPN needs to be passed to a neural classification architecture. Nevertheless, this is an accurate way of detection.
Pose estimation is a process that finds the joints present in a picture of a human being or an object. It also demonstrates the positions of the joints.
Semantic segmentation makes pictures more defined without the requirement of bounding boxes.
The concepts of Deep Learning and Artificial Intelligence are not new. Still, their potential and ability have not been utilized entirely. But in the near future, we can surely witness their actual potential. To know more about Deep Learning and Computer Vision check our blogs.