Computer Vision Applications

Introduction

Computers always follow human processes. Especially in this era of digitalization, where most processes use digital coding.

Deep learning neural networks is another popular technology. It’s famous for its accuracy in various applications.

Now we’ll discuss how Deep Learning Neural Networks use in computer vision applications.

Also will discuss the prominent use cases which have gained popularity are.

The Deep Learning revolution

As per reports, Deep Learning brings about a phenomenal change in every industry.

Nowadays, technology giants are investing heavily in R&D activities around it. The most popular companies working on deep learning projects include Google, Facebook & Microsoft. The Deep Learning Neural Networks model works like the human brain and has many hidden layers which can use to get better results. The more layers in a deep learning network, the more complex the task it can perform.

Deep Learning algorithms are the first to use in image recognition tasks. Later on, its used in other domains. It uses in speech recognition,  machine translation, and self-driving cars.

For self-driving cars, it detects lane markings, pedestrians, traffic signals, etc.

Computer Vision – an overview :

Computer vision is a kind of artificial intelligence application. It enables computers to interpret and understand digital images. Its uses in different industrial applications such as automatic inspection. Deep learning neural networks can perform computer vision tasks for different domains. It can use in natural language processing, speech recognition, video analysis, etc.

object recognition, image retrieval have also used this technique

Computer Vision Applications – Classification & Detection

Deep learning algorithms are used in computer vision applications. Which is further sub-divided into two categories –

1) Object Detection

2) Object Classification.

Deep Learning Neural Networks are also used in other computer vision applications. Image Segmentation and Video Analysis are some of the most important applications. Deep Learning has lots of scope in the field of computer vision. There are countless opportunities for researchers to explore new use cases with it. Deep Learning is used in vision applications. Pattern recognition and extraction are the most common use method.

Object Detection & Classification Deep Learning Algorithms

Object detection deals with identifying instances of specific objects within an image. Whereas Classification deals with assigning images to different classes. Deep learning neural networks use in advanced object detection to recognize images, videos. DL algorithms provide better accuracy than other machine learning algorithms.

The most popular computer visions applications are used in Deep Learning include ImageNet Classification. Facial recognition systems for security purposes, pedestrian detection application in driverless cars, etc.

Parking Guidance and Information (PGI) systems  

Parking Guidance and Information (PGI) systems are used in different parts of the world. PGI systems provide how many parking spaces are available in a particular area.

The system has a central server that collects data from the parking sensors installed in the area. A user interface that displays the information on a map or as text. Deep Learning algorithms use to detect and identify Parking spots in an image. The system can also use to generate below reports

• parking occupancy rates
• traffic congestion
• Other related statistics.

Deep Learning image recognition algorithms. Computer vision applications have a wide range of applications in the field of image processing. Deep Learning networks use in various segments like below

• image segmentation
• image classification
• image detection

This has immense scope for research. It can achieve better accuracy with greater speed. The more layers there are in a deep learning network, the lesser the errors will be.

This article will help to identify object/image recognition by using neural network experiments on image data sets.

Object Detection and Classification

Object Detection deals with identifying instances of specific objects within an image. Whereas Classification deals with assigning images to different classes. Deep Learning Algorithms use the below technique to recognize patterns within image data sets.

• Advanced image object detection
• Image classification techniques

Traffic Sign Detection algorithm

Traffic sign detection algorithms are used in smart cities around the world for road safety purposes.

These applications can detect various traffic signs based on an image recognition algorithm. Deep learning networks provide better accuracy than traditional machine learning algorithms…

Especially for image recognition tasks such as image classification and image segmentation.

ImageNet Datasets

The ImageNet Datasets are the most popular computer vision datasets. Which is used for training high-level image recognition models. It consists of 1.2 million images belonging to 1000 different classes. These Computer Vision Datasets are real-world attributes like accurate annotations, precise measurements, and hierarchical descriptions. This makes it suitable for performing various tasks including visual search, retrieval, and content-based image retrieval.

Many research works have used these datasets in recent years to train deep learning algorithms. It uses in different computer vision applications, including image classification, semantic segmentation, etc.

Datasets of Face Images

There are many face databases available on the internet, but few of them are popular due to the high-quality images they contain. The FERET database is the most popular face image dataset.

This uses in many research papers for training various computer vision applications.

It consists of over 10000 high-resolution face images with different facial features.

The CelebA dataset is another popular face image dataset with more than 200,000 celebrity faces from different parts of the world. These datasets are useful for training Deep Learning algorithms to detect and identify faces in an image.

High-Quality Datasets

There are many other high-quality datasets available. Those are not as popular as the ImageNet Datasets or the FERET database. But these are useful for training deep learning networks for computer vision tasks. Such as age- and gender classification, male vs. female Classification, etc.

The Age Dataset is the most popular Datasets for training Deep Learning algorithms.

Dataset Type

Annotations play a very important role in image Datasets. The algorithm takes these labels to train deep learning models for various computer vision tasks. Dataset annotations need accurate measurements of different facial features. Datasets featuring well-annotated images use in supervised learning problems with Real-World Attributes

1. Popular computer-vision datasets are the ImageNet Datasets, FERET database, and CelebA dataset, etc.
2. Deep learning networks provide better accuracy than other learning algorithms. Especially for image recognition tasks such as image classification and image segmentation.
3. Popular Datasets of Face Images are FERET database and Celebi dataset etc.
4. High-quality Datasets use for deep learning models. It includes Age dataset, Dataset with annotations, eye detection datasets, etc.
5. Popular face datasets featuring well-annotated images use in supervised learning problems.

Conclusion:

The computer vision field is progressing with the help of deep learning algorithms. But, good quality datasets are essential for training these networks. Its uses in different facial features and bounding boxes around faces. The Age dataset is a good example of such a dataset.