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.
- Classification is the process of categorizing an image into one of several categories in order to answer the query “What is in this picture?” (human, animal, object). A single category is allocated to each photograph.
- Localization, we can now pinpoint our object in the image, and our query now becomes, “Where is it?”.
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.
- Detection locates all of the items in an image and creates bounding boxes around them. Instance segmentation is a procedure that allows us to find the exact borders of our objects in specific cases, but that is a topic for another discussion.
What is YOLO (object detection algorithm)?
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.
- YOLO is prominent since it has a high degree of precision and can run in real-time. YOLO image processing and YOLO object tracking take only one forward propagation to run through the neural network to make predictions.
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:
- Classification-based algorithms. They are put in place in two steps. They begin by identifying regions of interest in an image. Second, they use convolutional neural networks to classify these areas. Because we must run forecasts for each specified region, this solution may be slow. The Region-based convolutional neural network (RCNN) and Fast-RCNN, Faster-RCNN, and Mask-RCNN are well-known examples of this sort of algorithm.
- Regression algorithms predict classes and bounding boxes for the entire image in one run of the algorithm. The YOLO family algorithms and SSD are two of the most well-known examples from this group. They’ve widely utilized in the area of real-time object detection. Why is that? Well, simply they give up a small percentage of accuracy in exchange for a lot of speed.
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:
- Center of a bounding box
- The value corresponding to a class of an object
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:
- During training and testing, YOLO sees the complete image.
- YOLO machine learning outperforming other top detection approaches when trained on natural photos
- YOLO AI model runs significantly faster than other detection methods