YOLO Object Detection Algorithm Overview

YOLO Object Detection Algorithm Overview

Let's have a look at the YOLO (You Only Look Once) real-time object recognition method, which is one of the most effective and incorporates many of the most original ideas from the computer vision research field. The capacity to detect objects is a crucial feature of autonomous vehicle technology. It's a field of computer vision that's blossoming and performing far better than it did only a few years ago. We'll see a few of recent upgrades to YOLO by the original developers of this significant technology at the end of this essay.
With the foundational 2015 publication “You Only Look Once: Unified, Real-Time Object Detection” by Joseph Redmon et al., YOLO burst onto the computer vision landscape, capturing the interest of fellow computer vision experts. In 2017, University of Washington researcher Redmon gave a TED talk about the state of the art in computer vision.

What exactly is YOLO?

Object detection is a classic computer vision issue in which you try to figure out what and where — specifically, what items exist inside a given image and where they are in the image. Object detection is a more difficult task than classification, which can distinguish things but does not tell where they are in the image. Furthermore, categorization does not operate on photos with many objects.

YOLO has a unique attitude to life. YOLO is a smart convolutional neural network (CNN) that does real-time object detection. The technique divides the image into areas and predicts bounding boxes and probabilities for each region using a single neural network applied to the entire image. The projected probabilities are used to weight these bounding boxes.

My articles is a family member  of guest posting websites which has a large community of content creators and writers.You are warmly welcome to signup and publish a guest post with a dofollow backlink no matter in which niche you have a business. Follow your favorite writers, create groups, forums, chat, and much much more!

YOLO is popular because it has a high level of accuracy and can run in real-time. The approach takes only one forward propagation run through the neural network to make predictions, so it "only looks once" at the image. It then returns detected items together with bounding boxes after non-max suppression (which ensures that the object detection algorithm only discovers each object once).

A single CNN predicts multiple bounding boxes and class probabilities for those boxes using YOLO. YOLO improves detection performance by training on entire photos. Compared to other object detection approaches, this model has a number of advantages:

YOLO is an acronym for "you only live once."

During training and testing, YOLO sees the complete image, so it implicitly encodes contextual information about classes as well as their appearance.

YOLO learns generalizable representations of objects, outperforming other top detection approaches when trained on natural photos and tested on artwork.

Here's an outstanding video example of YOLO's object detecting prowess:

A YOLO (You Only Live Once) Update

Further research was performed, resulting in the December 2016 study “YOLO9000: Better, Faster, Stronger,” by Redmon and Farhadi, both of the University of Washington, which improved the YOLO detection system by jointly improving detection and classification to detect over 9,000 item categories.

In April 2018, the same researchers published another paper, “YOLOv3: An Incremental Improvement,” with code available on a GitHub repo, detailing their progress with extending YOLO even further.

Much of the effort in deep learning research involves trial and error, as is the case with a lot of study. This effect was in full force during the quest of YOLOv3, when the team attempted a variety of various concepts, but many of them failed. A new network with 53 convolutional layers for feature extraction, a new detection metric, utilising logistic regression to predict a "objectness" score for each bounding box, and employing binary cross-entropy loss for class predictions during training are just a few of the things that stayed. As a result, YOLOv3 is substantially faster than other detection methods that provide comparable results. Furthermore, YOLO is no longer hampered by little items.

Academic Master is a US based writing company that provides thousands of free essays to the students all over the World. If you want your essay written by a highly professional writers, then you are in a right place. We have hundreds of highly skilled writers working 24/7 to provide quality  essay writing services  to the students all over the World.

I thought it was fascinating that the authors of the YOLOv3 paper, who are top-notch researchers who are actively pushing the boundaries of this technology, reflected at the end of the paper on how object detection is destined to be used:

“Now that we have these detectors, what are we going to do with them?” Many of the folks who are conducting this research work for Google and Facebook. At the very least, we know the technology is in good hands and won't be used to collect and sell your personal information to... wait, you're saying that's exactly what it will be used for?? Oh. The military, on the other hand, is a major funder of vision research, and they've never done anything heinous like mass murder with new technology, oh wait...”

Source: Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection.”

The CNN architecture for YOLO, which is inspired on the GoogLeNet model for image classification, is shown above. It's quite complicated. Andrew Ng's new specialised programme is highly recommended if you want to properly understand the theoretical foundations and underlying mathematics of this area of deep learning.

On OpenDataScience.com, you may find additional data science publications, including tutorials and tips for beginners to advanced users. Subscribe to our weekly email to receive the most up-to-date information every Thursday.