Using YOLOv3 for real-time detection of PPE and Fire

The ceaseless climb of the world’s skyscrapers is a story of ever-evolving challenges. We live our daily lives around skyscrapers, malls, and warehouses. Most of the time we are clueless about what it takes to construct these buildings. The construction industry is responsible for creating such magnificent masterpieces. With $1.3 trillion annual expenditure (~6.3% of the GDP) [1] and 7.6 million employees (~5% of the total workforce) [2] as of March 2020, construction is one of the largest sectors of the U.S. economy. However, it is also considered as one of the most hazardous industries due to the high construction site fatalities.

The top cause of construction related casualties are falls, people getting stuck in the equipment, electrocution and collisions . The majority of these injuries can be prevented if workers wear appropriate personal protective equipment like hard hats, safety vests, gloves, safety goggles, and steel toe boots.To ensure safety at the workplace, the U.S. Occupational Safety and Health Administration (OSHA) requires that contractors enforce and monitor appropriate usage of personal protective equipment (PPE).

Currently the PPE detection technique can be classified into two types: sensor based and vision based. The sensor based approach uses RFID (Radio Frequency Identification RFID) tags and requires a significant investment in purchasing, installing, and maintaining complicated sensor networks. On the contrary, vision-based approaches use cameras to record images or videos of the construction site, which are then analyzed to verify PPE compliance. This approach provides richer information about the scene that can be utilized to understand complex construction sites more promptly, precisely, and comprehensively.

This article explains how a deep learning model built on YOLOv3 (You Only Look Once) architecture can be used to meet various safety requirements like PPE compliance of construction site workers and fire detection using minimal hardware i.e. a surveillance camera. The deep learning algorithm simultaneously detects individual workers and verifies PPE compliance with a single convolutional neural network (CNN) framework. In today’s world, Convolutional Neural Network (CNN) is most widely used for image classification and object detection, due to its ability to self-learn from large sets of annotated training data.

Various real time object detection techniques

Any object detection problem in computer vision can be defined as identifying an object (a.k.a., classification) in an image and then precisely estimating its location(a.k.a., localization) within the image. Object Detection is used almost everywhere these days. The use cases are endless, be it tracking objects, video surveillance, pedestrian detection, anomaly detection, self-driving cars or face detection.

There are three main object detection algorithms that are currently used in the industry:

R-CNN: Region based Convolutional Network

An R-CNN first identifies several regions of interest, and then uses a CNN to classify those regions to detect objects in them. Since the original R-CNN is slow, faster variants of it, e.g. Fast R-CNN, Faster R-CNN, and Mask R-CNN have been proposed. In R-CNN, the image is first divided into approximately 2000 region recommendations (region propotals) and then CNN (ConvNet) is applied for each region respectively.

The size of the regions is determined and the correct region is inserted into the artificial neural network. The biggest problem of this method is time. Since each region in the picture is applied CNN separately, training time is approximately 84 hours and forecast time is approximately 47 sec.

YOLO: You Only Look Once

Most of the object detection algorithms use regions to localize the object within the image. The network does not look at the complete image. Instead, they look at parts of the image which have high probabilities of containing the object. YOLO or You Only Look Once is an object detection algorithm much different from the region based algorithms seen above. In YOLO, a single convolutional network predicts the bounding boxes and the class probabilities for these boxes.

A simplified illustration of the YOLO object detector pipeline (source)

The strategy used by YOLO is radically different from earlier detection systems. A single neural network is applied to the whole image. This network splits the picture into regions and calculates bounding boxes for each area and probabilities. Those bounding boxes are weighted by the predicted probabilities.

SSD: Single Shot MultiBox Detector

SSD is a popular algorithm in object detection. It’s generally faster than Faster RCNN. SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. [4]

Screenshot from article explaining the SSD Architecture

Now, we will see how we collected data for this project and the implementation of YOLOv3.

Data set Collection

In this project, we first classified the objects that we are detecting into four categories: Helmet (Person wearing helmet), Person( Person without helmet), Fire and Safety vest.

Image Labeling with YOLO format

Training data set are images collected as samples and annotated for training deep neural networks. We collected images for our data set from multiple sources like the safety helmet data set that can be found on github, images from google and Flickr and annotating them using labelIMg. Also, for getting custom data with these objects, we used the Flickr API & downloaded the images using python code.For labelling the image, we used the LabelImg tool.

#machine-learning #deep-learning #data-science #object-detection #yolov3 #data analysis

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4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

#analytics #artificial intelligence technologies #big data #big data analysis tools #from our experts #machine learning #real-time decisions #real-time analytics #real-time data #real-time data analytics

Implementing Real-time Object Detection System using PyTorch and OpenCV

Hands-On Guide to implement real-time object detection system using python

The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade.

Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together.

Python vs C++

Reading The Video Stream

Load the Model

Scoring a Single Frame

…

#artificial-intelligence #python #programming #implementing real-time object detection system #implementing real-time object detection system using pytorch and opencv #pytorch

Using YOLOv3 for real-time detection of PPE and Fire

The ceaseless climb of the world’s skyscrapers is a story of ever-evolving challenges. We live our daily lives around skyscrapers, malls, and warehouses. Most of the time we are clueless about what it takes to construct these buildings. The construction industry is responsible for creating such magnificent masterpieces. With $1.3 trillion annual expenditure (~6.3% of the GDP) [1] and 7.6 million employees (~5% of the total workforce) [2] as of March 2020, construction is one of the largest sectors of the U.S. economy. However, it is also considered as one of the most hazardous industries due to the high construction site fatalities.

The top cause of construction related casualties are falls, people getting stuck in the equipment, electrocution and collisions . The majority of these injuries can be prevented if workers wear appropriate personal protective equipment like hard hats, safety vests, gloves, safety goggles, and steel toe boots.To ensure safety at the workplace, the U.S. Occupational Safety and Health Administration (OSHA) requires that contractors enforce and monitor appropriate usage of personal protective equipment (PPE).

Currently the PPE detection technique can be classified into two types: sensor based and vision based. The sensor based approach uses RFID (Radio Frequency Identification RFID) tags and requires a significant investment in purchasing, installing, and maintaining complicated sensor networks. On the contrary, vision-based approaches use cameras to record images or videos of the construction site, which are then analyzed to verify PPE compliance. This approach provides richer information about the scene that can be utilized to understand complex construction sites more promptly, precisely, and comprehensively.

This article explains how a deep learning model built on YOLOv3 (You Only Look Once) architecture can be used to meet various safety requirements like PPE compliance of construction site workers and fire detection using minimal hardware i.e. a surveillance camera. The deep learning algorithm simultaneously detects individual workers and verifies PPE compliance with a single convolutional neural network (CNN) framework. In today’s world, Convolutional Neural Network (CNN) is most widely used for image classification and object detection, due to its ability to self-learn from large sets of annotated training data.

Various real time object detection techniques

Any object detection problem in computer vision can be defined as identifying an object (a.k.a., classification) in an image and then precisely estimating its location(a.k.a., localization) within the image. Object Detection is used almost everywhere these days. The use cases are endless, be it tracking objects, video surveillance, pedestrian detection, anomaly detection, self-driving cars or face detection.

There are three main object detection algorithms that are currently used in the industry:

R-CNN: Region based Convolutional Network

An R-CNN first identifies several regions of interest, and then uses a CNN to classify those regions to detect objects in them. Since the original R-CNN is slow, faster variants of it, e.g. Fast R-CNN, Faster R-CNN, and Mask R-CNN have been proposed. In R-CNN, the image is first divided into approximately 2000 region recommendations (region propotals) and then CNN (ConvNet) is applied for each region respectively.

The size of the regions is determined and the correct region is inserted into the artificial neural network. The biggest problem of this method is time. Since each region in the picture is applied CNN separately, training time is approximately 84 hours and forecast time is approximately 47 sec.

YOLO: You Only Look Once

Most of the object detection algorithms use regions to localize the object within the image. The network does not look at the complete image. Instead, they look at parts of the image which have high probabilities of containing the object. YOLO or You Only Look Once is an object detection algorithm much different from the region based algorithms seen above. In YOLO, a single convolutional network predicts the bounding boxes and the class probabilities for these boxes.

A simplified illustration of the YOLO object detector pipeline (source)

The strategy used by YOLO is radically different from earlier detection systems. A single neural network is applied to the whole image. This network splits the picture into regions and calculates bounding boxes for each area and probabilities. Those bounding boxes are weighted by the predicted probabilities.

SSD: Single Shot MultiBox Detector

SSD is a popular algorithm in object detection. It’s generally faster than Faster RCNN. SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. [4]

Screenshot from article explaining the SSD Architecture

Now, we will see how we collected data for this project and the implementation of YOLOv3.

Data set Collection

In this project, we first classified the objects that we are detecting into four categories: Helmet (Person wearing helmet), Person( Person without helmet), Fire and Safety vest.

Image Labeling with YOLO format

Training data set are images collected as samples and annotated for training deep neural networks. We collected images for our data set from multiple sources like the safety helmet data set that can be found on github, images from google and Flickr and annotating them using labelIMg. Also, for getting custom data with these objects, we used the Flickr API & downloaded the images using python code.For labelling the image, we used the LabelImg tool.

#machine-learning #deep-learning #data-science #object-detection #yolov3 #data analysis

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Why Use WordPress? What Can You Do With WordPress?

Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?

WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:

1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.

2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.

3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.

4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.

5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.

6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.

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#use of wordpress #use wordpress for business website #use wordpress for website #what is use of wordpress #why use wordpress #why use wordpress to build a website