How object detection works in YOLO architecture in deep learning

Biggest advantages

1. Speed (45 frames per second — better than realtime)
2. Network understands generalized object representation (This allowed them to train the network on real world images and predictions on artwork was still fairly accurate).
3. faster version (with smaller architecture) — 155 frames per sec but is less accurate .High level idea: Compared to other region proposal classification networks (fast RCNN) which perform detection on various region proposals and thus end up performing prediction multiple times for various regions in a image, Yolo architecture is more like FCNN (fully convolutional neural network) and passes the image (nxn) once through the FCNN and output is (mxm) prediction. This the architecture is splitting the input image in mxm grid and for each grid generation 2 bounding boxes and class probabilities for those bounding boxes. Note that bounding box is more likely to be larger than the grid itself.

What is YOLO

You Only Look Once (YOLO) is a state-of-the-art, real-time object detection algorithm introduced in 2015 by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi in their famous research paper “You Only Look Once: Unified, Real-Time Object Detection”.

The authors frame the object detection problem as a regression problem instead of a classification task by spatially separating bounding boxes and associating probabilities to each of the detected images using a single convolutional neural network (CNN).

By taking the Image Processing with Keras in Python course, you will be able to build Keras based deep neural networks for image classification tasks.

If you are more interested in Pytorch, Deep Learning with Pytorch will teach you about convolutional neural networks and how to use them to build much more powerful models

What Makes YOLO Popular for Object Detection?
Some of the reasons why YOLO is leading the competition include its:

1. Speed
2. Detection accuracy
3. Good generalization
4. Open-source 1- Speed YOLO is extremely fast because it does not deal with complex pipelines. It can process images at 45 Frames Per Second (FPS). In addition, YOLO reaches more than twice the mean Average Precision (mAP) compared to other real-time systems, which makes it a great candidate for real-time processing.

High detection accuracy
YOLO is far beyond other state-of-the-art models in accuracy with very few background errors.

3- Better generalization
This is especially true for the new versions of YOLO, which will be discussed later in the article. With those advancements, YOLO pushed a little further by providing a better generalization for new domains, which makes it great for applications relying on fast and robust object detection.

For instance the Automatic Detection of Melanoma with Yolo Deep Convolutional Neural Networks paper shows that the first version YOLOv1 has the lowest mean average precision for the automatic detection of melanoma disease, compared to YOLOv2 and YOLOv3.

4- Open source
Making YOLO open-source led the community to constantly improve the model. This is one of the reasons why YOLO has made so many improvements in such a limited time.

YOLO Architecture

YOLO architecture is similar to GoogleNet. As illustrated below, it has overall 24 convolutional layers, four max-pooling layers, and two fully connected layers.

The architecture works as follows:

1. Resizes the input image into 448x448 before going through the convolutional network.
2. A 1x1 convolution is first applied to reduce the number of channels, which is then followed by a 3x3 convolution to generate a cuboidal output.
3. The activation function under the hood is ReLU, except for the final layer, which uses a linear activation function.
4. Some additional techniques, such as batch normalization and dropout, respectively regularize the model and prevent it from overfitting . By completing the Deep Learning in Python course, you will be ready to use Keras to train and test complex, multi-output networks and dive deeper into deep learning.

How Does YOLO Object Detection Work?
Now that you understand the architecture, let’s have a high-level overview of how the YOLO algorithm performs object detection using a simple use case.

“Imagine you built a YOLO application that detects players and soccer balls from a given image.

But how can you explain this process to someone, especially non-initiated people?

→ That is the whole point of this section. You will understand the whole process of how YOLO performs object detection; how to get image (B) from image (A)

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