One of the many fascinating uses of convolutional neural networks is image classification. Apart from simple image classification, computer vision has a plethora of exciting issues, with object recognition being one of the most intriguing.
It’s most usually connected with self-driving cars, in which systems use computer vision, LIDAR, and other technologies to create a multidimensional representation of the road and all of its users. Object detection is also widely used in video surveillance, particularly in crowd surveillance to prevent terrorist acts, count individuals for general statistics, and analyze consumer experience.
Image classification progresses through a series of more difficult stages.
In a real-life situation, we’ll need to locate numerous objects in a single photograph rather than just one. A self-driving automobile, for example, must locate other cars, traffic signals, signs, and humans and take appropriate action based on this information.
So, 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.
Following non-max suppression, which ensures that the object detection algorithm only identifies each object once, the recognized objects and bounding boxes are output.
A single CNN predicts multiple bounding boxes and class probabilities for those boxes using YOLO. YOLO improves detection performance by training on entire photos.
There are a couple of distinct object detection methods, which can be divided into two groups:
To comprehend the YOLO algorithm, one must first determine what is being forecasted.
Finally, we want to be able to forecast an object’s class and the bounding box that defines its placement.
Four descriptors can be used to describe each bounding box:
As previously stated, we are not looking for interesting parts in our image that could potentially contain an object while using the YOLO technique.
Rather, we divide our image into cells, usually using a 19×19 grid. If there is more than one object in this cell, each cell is responsible for predicting five bounding boxes.
As a result, for one image, we end up with a high number of 1805 bounding boxes.
The majority of these cells and boundary boxes will be empty. As a result, we forecast the value pc, which is used in a method known as non-max suppression to bounding boxes with the highest common area.
YOLO model object detection has a number of advantages when is compared to other object detection approaches: