YOLO Algorithm For Object Detection: A Simple Guide (2021)

Introduction

One of the several branches of Data Sciences and Computer Vision is Object Detection, and YOLO Algorithm is a subset of it. Let us take up simple examples to get introduced to the concept. We use a ‘Camera’ to get the best memories shot in a frame on every occasion. These devices use an algorithm to detect faces and ensure that the camera lens’s focus is on the faces or any object that we want to prioritize in the captured image.

Another example is self-driving cars. They are programmed to locate all necessary obstacles that are required to get them functioning well. Be it the signals, other vehicles, speed-breakers, road-side signboards, and many more, which help them operate independently. How do you think this is possible? Well, this is all about Object Detection.

YOLO (“You Only Look Once: Unified Real-Time Object Detection”) is one such real-time Object detection algorithms. It was first described in the seminal 2015 paper by Joseph Redmon et al., where the concept of YOLO was determined and its implementations, ‘Darknet’ was discussed. Over time, there are many improvements made in the YOLO algorithm giving birth to YOLOv3, PP-YOLO, YOLO9000: Better, Faster, Stronger.

In this article let us look at:

1. Overview of Object Detection
2. YOLO Algorithm
3. YOLO Implementation – Darknet

1. Overview of Object Detection

Object Detection is often discussed along with Image Classification and Object Localization. When the device captures an image, it first classifies the image into different categories, which determines what the picture is. For example, a human, a bus, a toy, an animal, a bird, a table, etc. This process is termed ‘Image Classification’. After this, the further process involves the location of the object in the image termed as ‘Object Localization’.

This process involves screening for where exactly the object is placed in the entire image. And the last function is ‘Object Detection, which enables the drawing of ‘bounding boxes’ around the located objects. A more precise version of ‘Object Detection’ is termed as ‘Instance segmentation’ wherein exact outlines of the detected object are drawn.

2. YOLO Algorithm

To understand the YOLO algorithm better, let us first understand the various other types of algorithms simultaneously.           

• Algorithms based on Classifications

These algorithms use two-stage methods. Firstly, interested regions are selected. Secondly, they are classified using Convolutional Neutral Networks by running predictions. For Example, Region-based Convolutional Neutral Networks (RCNN), Fast-RCNN, Faster-RCNN, Mask-RCNN, and RetinaNet.

• Algorithms based on Regression

This implements a single-step process wherein, in one run, the image is screened, and objects are located with the bounding boxes as well as their class is predicted in the same run. These are generally used for real-time detection where time, speed, and accuracy form prime concerns—for Example, YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector).

Hence, YOLO (You Only Look Once) Algorithm uses the regression-based technique, and as its name suggests, it detects and performs operations in a single run with the application of Artificial intelligence and Deep Learning.

Each bounding box in a YOLO algorithm consist of four descriptions:

• Centre of the bounding box (b_xb_y)
• Width (b_w)
• Height (b_h)
• Class of object (c)
• The Probability that there is an object in the bounding box (p_c)

Example,

y=(p_c, b_x, b_y, b_h, b_w, c)

The YOLO algorithms, unlike other algorithms, involve the splitting of the image into multiple cells and depending upon how many objects are covered in the image, multiple bounding boxes are predicted by each cell. This causes the creation of a large number of bounding boxes, and in this process, there may arise bounding boxes that do not contain any object at all or also intersected bounding boxes that share the same spaces of the image. To get rid of this issue, a non-max suppression technique is used wherein such shared spaces are nullified, and also p_c value is predicted to identify the boxes with no objects and ensure their removal.

YOLO model evolved on the basis of the Pascal VOC detection dataset. The initial layers of Convolution help extract feature from the image, and the fully connected layers predict the output probabilities. It consists of 24 convolutional layers along with 2 fully connected layers.

3. YOLO Implementation – Darknet

Written in C language and CUDA technology, Darknet provides fast computations on GPU and a highly accurate framework for real-time object detection. It is an Open Source neural network framework that is easy to install.

Conclusion

Through the entire write-up, it is evident that YOLO has its benefits over other algorithms as enlisted below:

• It is swift and accurate.
• It encodes contextual information about Object classification as well as Object appearance in a single run.
• It enables generalizable representations of objects which in turn makes the algorithm outperform others.

The earlier versions of YOLO struggled with small objects in the image, but the latest versions have overcome this issue as well.

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