I recently started working in the field of computer vision. And in these early days, I'm studying how the various algorithms of object detection work. Among the most well-known ones are R-CNN, Fast R-CNN, Faster R-CNN and of course YOLO. In this article, I want to focus on the last mentioned algorithm. YOLO is the state of the art in object detection and there are endless use cases where YOLO can be used.

For this story, we will take a deep look into the YOLOv4, the original paper is huge and has a ton of things. So, fasten your seat belts as it is going to be an extremely long blog to follow just because of the sheer number of things presented in the YOLOv4 paper. All the YOLO models are object detection models, and with the release of YOLOv4, there is a significant increase in the inference time of the model. It can be trained on a single GPU as that is what the authors initially set out to do. I'm assuming that you know how YOLO works for those who want to know it in more detail can look here.

We all know that cryptocurrencies are the only way to do anonymous transaction. Either it is FBI or NIA, they find it impossible to track the record of transaction made through cryptocurrency. Every technology geek in today's time have tried to understand the concept of cryptocurrency due to its growing popularity. In many countries, the cryptocurrency is banned but still it a trending currency in the cyber criminal underground world. But do you wonder what makes it trending in the underground?

As the spread of COVID 19 continues, communities are told to reduce close contact between individuals. This is called Social Distancing, as it is a necessary and effective way to slow down the spread of the virus. As a data science student, I came with a solution to identify whether people actually follow the social distancing protocol of staying at least 6 feet from each other. YOLO stands for You Only Look Once. It's a fast operating object detection system that can recognize various object types in a single frame more precisely than other detection systems.

What is the best way to mark objects: label only the visible part of the object, or label the visible and overlapped part of the object, or label a little more than the entire object (with a little gap)? Mark as you like - how would you like it to be detected.

Some time ago, I spent several evenings playing around with state of the art object detection model called YOLO, which is certainly known to those who are interested in Machine Learning on a daily basis. Originally written in Darknet -- open source neural network framework -- YOLO performs really well in the tasks of locating and recognizing objects on the pictures. Due to the fact that I have been interested in TensorFlow.js for a few weeks now, I decided to check how YOLO will handle the limitations of In-Browser computing. If you want to play with the demo version, visit "I Learn Machne Learning" project website. A few months ago, the third version of YOLO was released.

YOLOv3 is one of the most popular real-time object detectors in Computer Vision. In our previous post, we shared how to use YOLOv3 in an OpenCV application. It was very well received and many readers asked us to write a post on how to train YOLOv3 for new objects (i.e. In this step-by-step tutorial, we start with a simple case of how to train a 1-class object detector using YOLOv3. The tutorial is written with beginners in mind. Continuing with the spirit of the holidays, we will build our own snowman detector.

Owning a quality camera can be fairly useful by itself. Video stream can provide a lot of information not easily comprehendable by just using various sensors. However, not always there is a human eye to make a sense of it. Therefore, additional algorithms can be implemented to provide a lot of insights automatically. A lot of them can be extracted and tracked using detection algorithms.

KOCHI: It could well be a harbinger of'machine learning'. In tune with the'technology' strewn around the venue of Cocon 2018, a robot stepped forward to deliver an inaugural speech at Hotel Grand Hyatt on Friday. The annual two-day international cybersecurity conference organised by the Kerala Police, Information Security Research Association and the Society for the Policing of Cyber space (POLCYB) literally marked the advent of artificial intelligence and machine learning. The Robot- Inkar Sabot- struggled a bit to adopt its technical expertise. But it made the audience awestruck when it started delivering the speech in a pre-loaded UK accented English. State police chief Loknath Behera, who was the chief guest of the international conference, said with the darknet (a computer network with restricted access using for illegal peer-to-peer file sharing) posing a threat, the Kerala Cyberdome is all set to intrude into it to stop the criminal activities.

In this post, we will learn how to use YOLOv3 -- a state of the art object detector -- with OpenCV. YOLOv3 is the latest variant of a popular object detection algorithm YOLO – You Only Look Once. The published model recognizes 80 different objects in images and videos, but most importantly it is super fast and nearly as accurate as Single Shot MultiBox (SSD). We can think of an object detector as a combination of a object locator and an object recognizer. In traditional computer vision approaches, a sliding window was used to look for objects at different locations and scales.