YOLO’s Real-Time Object Detection

The study of how computers can interpret and comprehend the visual environment is known as computer vision in the field of artificial intelligence. It seeks to comprehend and computerize actions that the human visual system can perform. By 2024, the computer vision market is anticipated to rise from $10.9 billion in 2019 to $17.4 billion, expanding at a CAGR of 7.8%. (1). YOLO’s real-time object detection is the next step for this technology, understand how and why it works.

Real-Time Object Detection

There are numerous industries where computer vision is used; some of the more well-known ones include:

Automotive – To improve its self-driving cars, companies like Google and Tesla use computer vision cameras.

Retail & Retail Security – The main applications are inventory management automation, customer foot traffic analysis, and theft security.

Healthcare – Early cancer diagnosis and cancer prediction are made possible with the aid of computer vision.

Agriculture: Using precision farming and early crop disease detection to increase yield

Identifying manufacturing flaws that are invisible to the naked eye in the manufacturing industries

A scene in computer vision is a representation of a real-world environment with a variety of surfaces and objects. Various tasks that can be included in scene comprehension are:

Identifying an object’s class through classification of a single object

Locating an object while also determining its class is known as “classification + localization of an object.”

Multiple item localization and classification is known as object detection.

Identifying and classifying the precise instances of the object through instance segmentation

Object detection will be the main topic of this paper. Let’s examine some of the most important object detection applications.

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Autonomous Vehicles

vehicles that can drive themselves without human assistance. The car can detect other objects, such as other vehicles, pedestrians, etc., thanks to object detection systems. Waymo, a subsidiary of Google, is the industry leader in self-driving vehicles, having traveled more than 20 million miles in the US. Other significant stakeholders include businesses like Tesla and General Motors.

Face Recognition & Face Detection

Face Detection & Recognition has a wide range of uses, including controlling access to sensitive locations, forensic investigation, identity verification at ATMs, and security applications. Face-detection algorithms are used by Snapchat, Instagram, and Facebook to apply filters and identify you in photos.

Tracking of Objects

The objects are tracked using object detection. Examples of this type of tracking include following the path of a cricket bat, a football during a game, or a person in a film. There are several applications for object tracking, including traffic monitoring, security, and surveillance.

Real-Time Object Detection

Humans are capable of quick glances around objects. In actuality, this is real-time object detection. The algorithm should be quick to identify things and draw conclusions for issues like self-driving automobiles. An average video contains about 24 frames per second (fps), so an algorithm that can detect something in real time must be faster than this rate.

The following categories can be used to classify object detection algorithms:

Algorithms Based on Region Proposals—These algorithms are implemented in two steps. The image’s region of interest is first chosen. Second, convolutional neural networks are used to categorize these regions. Because we have to make forecasts for each location that has been chosen, this solution takes a while. The Region-based Convolutional Neural Network (RCNN) family of algorithms includes Fast-RCNN and Faster-RCNN. Another illustration of this kind of algorithm is RetinaNet.

These methods function well when real-time detection is not required, but because of their poor pace, they frequently struggle with real-time detections.

Single Stage Algorithms: These algorithms are employed for real-time object detection since, on average, they sacrifice some accuracy for significant speed increases. There is no bounding box proposal and following pixel or feature resampling phases, unlike the RCNN family. In a single evaluation, a neural network can immediately predict bounding boxes and class probabilities from entire images. The SSD and YOLO (You Only Look Once) family algorithms are the best examples of this category (Single Shot Multibox Detector).

We will concentrate on the real-time detection YOLO technique in this post.

Real-Time Object Detection with “You Only Look Once”, or YOLO

The cutting-edge, in-the-moment object detection system called YOLO is built on the darknet infrastructure. A faster version of YOLOv3 processes images at about 150 frames per second on a Pascal Titan X and has a mAP (mean Average Precision) of 57.9 percent on COCO test-dev(2) (with less mAP). YOLOv3 is suitable for real-time object detection because it is 100–1000 times quicker than fast RCNN and maintains accuracy. The moniker YOLO comes from the fact that the entire forecast is made in a single image evaluation.

  • Performance Evaluation in Relation to Other Detectors

YOLOv3 is on par with mAP when compared to the other detectors but is substantially faster.

(2) In addition, we can easily compromise between accuracy and speed by by altering the size of the model.

The input image is divided into S X S grid cells by YOLO. A grid cell is in charge of detecting an object if its center falls within that cell. B bounding boxes, confidence ratings for those boxes, and C conditional class probabilities are predicted for each grid cell. We compute class-specific confidence ratings for each box by multiplying the conditional class probabilities by the individual box confidence forecasts at test time.

We must first comprehend what YOLO predicts, i.e., the elements of bounding boxes and anchor boxes, in order to comprehend the YOLO algorithm.

Bounding Box – Yolo divides the input image into a S x S grid, for example, a 3 x 3 grid. Let’s presum a trinity of Person, Car, and Truck object types. The anticipated label y for each grid cell will be an eight-dimensional vector.

  • Processing YOLO

After thoroughly understanding the meanings of bounding and anchor boxes, let’s examine the method through which YOLO creates predictions.

  • Image processing input

Two fully connected layers are applied after two Convolution and Maxpool layers in a deep CNN network to produce the output image (Figure 9).

Let’s examine an illustration to better comprehend this (Figure 10). A 19×19 grid with five predetermined anchor boxes is utilized to divide the input image in this instance (generally used, might differ as per YOLO version). There will be a total of 1805 anchor boxes, and the network will generate 85 anticipated elements for each anchor box.


The most advanced real-time object detection method is YOLO since it is substantially faster than previous algorithms while yet maintaining a high level of accuracy.

Although the YOLO network is capable of understanding generalized object representation, the accuracy is limited for adjacent and smaller objects due to spatial restrictions. This issue is addressed in a more recent iteration of the algorithm called YOLOv4, which is also faster and more accurate. Overall, YOLO is a popular approach for real-time object recognition because to its speed and accuracy.

Python: Real Time Object Detection (Image, Webcam, Video files) with Yolov3 and OpenCV

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