Difference between yolo and convolutional neural networks

What does yolo algorithm mean?

YOLO (YouOnlyLookOnce), is a network for target detection.

The task of target detection consists of determining the location of the presence of certain objects in an image, as well as classifying these objects. Previous approaches, such as R-CNN and its variants, use a pipeline to perform this task in multiple steps. This can run slowly and is difficult to optimize because each individual component must be trained separately.


YOLO reframes object detection as a regression problem. It applies a single convolutional neural network (CNN) to the entire image, divides the image into grids, and predicts class probabilities and bounding boxes for each grid.

The algorithm also predicts the probability of an object being present in the bounding box. If the center of an object falls in a grid cell, that grid cell is responsible for detecting the object. There will be multiple bounding boxes in each grid. During training, we would like to have only one bounding box per object. Therefore, we assign a Box to be responsible for predicting an object based on which Box has the highest overlap with the groundtruthbox.

What is the yolo algorithm?

YOLO is an algorithm that uses neural networks to provide real-time object detection. The algorithm is popular for its speed and accuracy. It has been used in various applications to detect traffic signals, people, parking meters, and animals.

YOLO is an acronym for the term “YouOnlyLookOnce”. It is an algorithm that (in real time) detects and recognizes various objects in an image.Object detection in YOLO is done as a regression problem and provides the class probability of the detected image.

The YOLO algorithm uses a convolutional neural network (CNN) to detect objects in real time. As the name suggests, the algorithm requires only one forward propagation through the neural network to detect an object.

This means that the prediction in the entire image is done in a single algorithm run.The CNN is used to predict various class probabilities and bounding boxes simultaneously.

YOLO algorithm consists of various variants.


1. Speed: This algorithm improves the speed of detection as it predicts objects in real time.

2. High accuracy: YOLO is a predictive technique that provides accurate results with minimal background error.

3. Learning capability: The algorithm has excellent learning capabilities that allow it to learn object representations and apply them to object detection.

What is the yolo algorithm?

Yolo algorithm uses a single CNN model to achieve end-to-end target detection.

First, the input image is resized to 448×448, then fed into a CNN network, and finally the network prediction results are processed to get the detected target. Compared to R-CNN algorithm which is a unified framework, its faster and Yolo’s training process is also end-to-end.


Yolo uses a convolutional network to extract features, and then uses a fully connected layer to get the predicted values. The network structure refers to the GooLeNet model, which contains 24 convolutional layers and 2 fully connected layers, as shown in Figure 8. For the convolutional layers, 1×1 convolution is mainly used for channlerection, and then followed closely by 3×3 convolution.