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Machine Learning

Computer Vision trends that will dominate the industry in 2021

Computer Vision trends in 2021

During the current pandemic, the one thing which every brand adopted is a quick digital transformation. The transformation which would have taken place in the next 5 years, just happened in the past six months. This accelerated adoption will continue in 2021 in the areas of intelligent industrial automation and artificial intelligence.

Video analytics powered by computer vision will revolutionize risk management and mitigation, automated monitoring, and security, which will achieve operational efficiency in industrial environments. Keeping the prospects of growth in mind, amalgamation, or advanced computer vision with different techs will dominate the year 2021.

Here are the Top 6 Computer Vision Trends that will dominate the industry in 2021

  1. Make way for safety: Ensure public and workplace safety

Ensuring safety in every organization is very important. Therefore, safety protocols and new daily routines have been introduced for improving the safety programs approach. Currently, technology is playing a significant role in facilitating those enforced changes. And vision intelligence is further being utilized by many industries around the globe for implementing safety.

HSE video anomaly detectors have been proven effective for automated monitoring and analysis for finding anomalies like absence of Safety Masks, PPE Kits, and other regulations like social distancing for employee safety.

  • Root for Quality Inspections: Automate Anomaly Detections

The largest electronic manufacturers have adopted the technologies for automating production monitoring and defect detection. High-quality images are produced; printed circuit boards are utilized for checking 20+ anomalies and defects.

Other industries like Automotive, Food and Beverage, and steel are leveraging computer vision for optimizing visual inspection and automation.

With a laid-off workforce and declining profit margins, 2021 would be the crucial year when more industrial leaders who are looking to utilize Computer Vision and AI inspections would gain golden quality, flexibility, accuracy and low cost, which the technology brings.

  • Opt for Non-destructive Testing: Utilization of Thermal Cameras

Augmented non-destructive testing computer vision is a solution which detects defects and marks the area of interest if there is a high probability for defined defects or anomalies, making use of radiology images that are taken via NDT techniques.

Automated Vision, which is based on inspections, widens the visible spectrum, and detects the metal surface defects which are often invisible to the human eye.  Another fascinating applied thermal imaging data application is to recognize the surface cover of the Peruvian Andes glaciers, which shrunk by 30 % in the past few decades. Due to its melt rate, it’s a serious threat to the water supply for the people living in the Ancash region of Peru.

Advanced computer vision applications and deep learning technologies would further help in analysis and experiments in the upcoming years.

  • Gain in real-time: The advancement of Edge Computing

The rise of edge computing is quickly solving the problems of network accessibility and latency. This also helps in better real-time response and move with relevant insights to the cloud for further analysis.

It enables engineers, trainers, team leads, and quality teams of lining operators for examining every step of the manufacturing process with real-time video analytics. This saves a lot of time needed for manual cycle time monitoring and also optimizes the production cost.

  • Look for helping hands- Sensor Data Triangulation

Video Analytics is unleashing a new frontier for automating surveillance cases in the Military and Defense. The ability to detect events and alert the security has contributed to the physical security at national borders. 

Advanced perimeter monitoring system gathers several forms of data such as video feeds, sensor data, and drone imagery from various touchpoints and triangulates them for providing real-time insights. This integration offers a multi-layered security system with robust features of unidentified object detection, intrusion detection, vehicle detection, and user access control. 

  • Opportunity of Automation- The Closed Loop Solution

We have witnessed rigorous development in the last decade. And one simple example is automatic user access control by facial recognition. 

The advancement of vision-based control is realized in the developments of autonomous cars and unmanned vehicles. Vision system controls the vehicle movements in real-time for any user-defined and desired inputs by making use of visual feedbacks only when conventional sources of accurate position or orientation data are not available. 

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Advanced computer vision with different techs will dominate the industry in 2021. Check out the top 6 computer vision trends now to know about the thriving world of AI, ML and deep learning. 

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Categories
Bounding Box

What is Object Detection?

Object Detection

Object detection is a part of Computer Vision technology that helps in identifying and locating objects in videos or images. Humans can easily locate and recognize objects of interest within few seconds. Similarly, object detection algorithms locate instances of objects in a given image thereby allowing machines to replicate the human vision.

Object detection and image recognition are often used interchangeably but are two different entities with a clear distinction between them. While image recognition is used for labeling images, object detection draws a shape like a box around the object and then labels the box. Moreover, object recognition identifies where each object is and what label is applicable thereby giving more information than image recognition.

How Does Object Detection Work?

We will explore some of the simple algorithms that are used for object detection to understand how it works;

R-CNN

R-CNN proposes bounding boxes in the image and verifies if any of these boxes have any objects. It comprises of three modules which are as follows:

Region Proposal

R-CNN algorithm uses Selective Search approach for extracting boxes/regions from an image. The Selective Search identifies 4 basic regions of an object such as colors, textures, scales, and enclosures and proposes various regions based on these patterns. Below is the step-by-step brief of Selective Search works;

  • It generates sub-segmentations initially which helps to identify multiple regions from an image.
  • Later, it merges similar regions to form a larger region based on colors, textures, scales, and enclosures.
  • These larger regions then help to identify the region of interest or object location.
  • It extracts about 2000 region proposals from an image.

Feature Extraction

The proposed regions are then fed into a CNN-based classifier where the regions are reshaped as per the input of CNN. It then extracts feature vector having fixed-length from each region.

Classifier

Linear support vector machines are finally used to classify each region in an image  

Fast R-CNN

Similar to R-CNN, this approach also uses the Selective Search for generating object proposals. But the architecture of Fast R-CNN supports single-stage training, has higher mean average precision, feature caching doesn’t require disk storage and training helps to update all network layers.

  • Fast R-CNN takes the image and object proposals as input and processes the image with max-pooling and convolutional layers to generate convolutional feature maps.
  • This is followed by the extraction of fixed-length feature vector from each of the feature maps which are then fed to fully connected layers.
  • Two output layers are used on top of a fully connected network; a softmax layer to output classes and a linear regression layer to output bounding box coordinates for classes.

Faster R-CNN

Faster R-CNN is a modified version of Fast R-CNN that uses Region Proposal Network (RPN) for generating Regions of Interest instead of Selective Search;

  • An image is passed on to the convolutional layer as input which helps to generate feature maps of that image.
  • Feature maps are run through the RPN which generates object proposals with an objectness score.
  • To bring down all proposals to the same size, object proposals are run through the ROI pooling layer.
  • Proposals are then passed on to the fully connected network where the Softmax layer outputs the classes and the linear regression layer outputs bounding boxes for objects.

Use Cases

Object detection has been already put to use in the following areas:

Self-driving Cars

Self-driving cars should have the ability to detect, locate and track objects surrounding them to move on the roads efficiently and safely. For this, they rely heavily on object detection models. The success of autonomous vehicular systems depends on the accuracy of car detection models that can detect in real-time.

Even though data labeling techniques like image segmentation also helps to train autonomous vehicles, object detection acts as the foundation for making self-driving cars a reality.

Video Surveillance

World-class object detection techniques can detect and track multiple instances of an object in a scene accurately and hence form the basis for automated video surveillance systems. These models can detect and track various people all at once and in real-time as they move across video frames. This kind of granular tracking helps to provide actionable insights for the performance and safety of workers, security, foot traffic at retail outlets, etc for retail stores, factory floors in the industrial sector, etc.

Detection of Anomalies in healthcare, agriculture

Object detection models can be used for acne treatment where the model helps to locate and detect the instances of acne within few seconds thereby helping to treat specific skin conditions.

Custom object detection models can be used to detect and identify potential instances of crop or plant diseases that help farmers to identify threats to their yields which are otherwise non-detectable by the naked human eye.

Crowd Counting

Crowd counting is a valuable use case of object detection that helps to localize and track people as they move through various spaces. It helps businesses to measure various types of traffic in densely populated areas like malls, city squares, and theme parks.

Object detection can help businesses optimize their store timings, inventory management, logistics pipelines, and shift scheduling.

About Data Labeler

Data Labeler specializes in providing high-quality data labeling services and is one of the top data annotation companies in Philadelphia. Are you for looking Machine Learning Training Data to train your AI-based algorithms and models? Reach out to us at sales@datalabeler.com for top-quality data labeling services.

Categories
Machine Learning and Deep Learning

What is the Training Data?

Training Data

Machines are replacing humans in routine and manual jobs because of the faster processing speed and storing knowledge advantage they have over humans. One can even leverage their speed and turn them into intelligent machines. Here is where the Training data comes into the picture. By feeding them with relevant data, machines can be trained to mimic the human brain and learn to process information.

Training data even though is a simple concept, forms the basis to the way cutting-edge technologies like machine learning and deep learning programs work. It is an initial dataset that helps a program or an algorithm find relationships, understand, learn and produce sophisticated results.

The performance of the ML and DL models depends on the quality and quantity of the training data.

Why Training Data Matters?

One can describe training data as well-structured or labeled data that helps to sharpen your ML models.  You will require vast amounts of data to train your models with high accuracy.

A great model requires training data at a large scale and has to be labeled in a way that will work for training your algorithm or model. By feeding the self-driving car models with a picture of the road won’t be enough. They should be fed with labeled images where every object such as a street sign, vehicle, pedestrian and more have to be annotated.

In case of projects that require sentiment analysis, the algorithm has to be fed with labeled data that will help it to understand sarcasm or slang.

How to collect Training Data?

Data Labeler can be a good partner in your quest for training data. We have the expertise and experience in labeling millions of images and videos daily for some of the top innovative companies in the world.

Whether you are looking for text, image, video or any kind of data annotation services, we are here to help you in collecting world-class training data for any industry.

From autonomous vehicles and drones to agriculture, retail and sports analytics, we are adept at supporting all image and video annotation types. We specialize in the following:

  1. Bounding Box Annotation
  2. Polygonal Annotation
  3. Semantic Segmentation
  4. Cuboid Annotations
  5. Line annotation
  6. Text annotation
  7. Select & Multi-select annotation

Looking for a FREE consultation? Reach out to us at sales@datalabeler.com for top-quality data labeling services.

Categories
Natural Language Processing

What is Natural Language Processing (NLP) and What are its Uses?

Natural Language Processing

Natural Language Processing is a branch of Artificial Intelligence that enables the machines to read, understand and interpret the human language. Its main focus lies in the interaction between human language and Data Science.

Most of the techniques used in NLP depend on Machine Learning and Deep Learning to extract value from human language.

How NLP Works?

The first step in NLP depends on the type of application being used. In the case of voice-based systems, the first step involves the translation of words into text mainly using Hidden Markov Models (HMM). HMM involves usage of math models to understand what you said and translate into text which is then processed by the NLP system.

The next step involves understanding the context and the language by breaking every part of the sentence into its part of speech. A series of coded grammar rules that depend on algorithms are used for this step. These algorithms use statistical ML to help the NLP system understand the context of the word.

In the case of other scenarios where speech-to-text is not involved, the NLP system skips the first step and moves directly into interpreting words using grammar rules and algorithms.

NLP uses two main techniques for understanding human language; Syntax and Semantic analysis.

Syntax involves the arrangement of words to make sense grammatically. Syntax analysis enables NLP to derive meaning from a language based on grammatical rules.

Some of the syntax techniques include the following;

  • Parsing – Analyzing a sentence for grammar
  • Sentence breaking – Placing sentence boundaries for large texts
  • Word Segmentation – Dividing a large piece of text into smaller units
  • Morphological Segmentation – Dividing words into groups
  • Stemming – Dividing words with inflection to its root forms

The semantic analysis involves the extraction of exact meaning from the text. It helps the NLP system to understand the meaning and structure of sentences and to interpret human language logically.

NLP uses the following semantic techniques to understand sentences:

  • Sense Disambiguation – Deriving the meaning of a word using its context
  • Named Entity Recognition – Helps to identify the words that can be categorized into groups
  • Natural Language Generation – Usage of a database to extract semantics behind words

Common Uses of NLP

Chatbots

NLP can help improve the chatbots by training them for a particular behavior before deploying them. Chatbots use NLP algorithms for answering customer queries. These algorithms help the chatbots to understand a customer query and answer to those queries automatically in real-time.

Sentiment Analysis

Sentiment Analysis is a common application of NLP that can determine the positive or the negative polarity of a text. It can be used to classify reviews of a company or its products or poll customer’s opinion based on their social media posts and comments. This helps to provide customer insights on products or services.

NLP cannot single-handedly perform this task, it requires integration with ML and DL to perform back-end computation and data analytics to understand the data on a large scale.

Email Assistant

Grammar and spell check, auto-correct and auto-complete are some of the everyday use cases of NLP. Email filtering that keeps the spam mails away also uses NLP to determine the type of emails to keep in your inbox and sort out the spam mails.

About Data Labeler

Data Labeler specializes in providing high-quality data labeling services and is one of the top data annotation companies in New Jersey. Are you for looking Machine Learning Training Data to train your AI-based algorithms and models? Reach out to us at sales@datalabeler.com for top-quality data labeling services.