Overview
What if you are watching a cricket match and the batsman gets LBW out but the umpire is unable to make any decision so it refers the decision to the third umpire who checks it on the device. The device tracks and detects the movement of the ball and the spot where the ball hits. The umpire viewed and monitored the ball from all angles to make a decision. This is done by Object Detection, one of the applications of Computer Vision and Deep Learning.
Object Detection can be defined as the ability to recognize and locate objects in images, and videos. It is expanded to multiple applications in diverse sectors such as video surveillance, person detection, face recognition, cancer detection, autonomous vehicles, and many more. These types of amazing and fruitful use cases are in action from the outstanding work done by programmers and developers.
In this article, I’ll provide some basic information about object detection, How it works, and shed light on some of its real-life applications. So let’s move forward with it
What is Object Detection
Object Detection is a computer vision technology that is used to recognize and locates objects within the image or video. During the last few years, due to its flexibility object detection has come up to be the most commonly used computer vision technique. The main goal of object detection is to develop algorithms that help computers and applications to understand and locate which type of object is located where precisely and accurately just as humans do.
As technology is frequently evolving there are a few terminologies related to computer vision that are often interlinked and commonly create confusion. Such as object detection being confused by image recognition, so before we proceed further let’s understand the primary difference between both.
In image recognition, a label is assigned to an image let’s suppose a picture of a cat received the label “cat”. A picture of two cats still receives the label “cat”. But in object detection bounding boxes are made around the object in an image and locate all the objects present in an image. Object detection will add a box around each cat in the image.
How Object Detection Works
So far, You had grasped some basic knowledge of object detection. Let’s look into how object detection works. Object detection models label the image or video taken as input by adding bounding boxes on detected objects. Deep learning and machine learning are two approaches used for object detection. In the machine learning, object detection method objects within the image are detected manually using the algorithms like Histograms of Oriented Gradients(HOG), Speeded-up- robust feature(SURF), and local binary patterns(LBP).
Object detection with deep learning is considered to be the most intuitive approach and can detect features automatically. One key component that makes the deep learning approach different is the involvement of convolutional neural networks(CNN). CNN is a type of neural network that has the capability to work and understand as human brains do. Each CNN is consist of convolutional layers(input layer) having several filters to extract different features from the input image. The output layer is a set of feature map.
Object Detection Models and Algorithm
Deep neural networks produce fast and better results using different models let’s discuss a few of them
R-CNN
Region convolutional neural networks(RNN) are one of the families of CNN defined in 2014. The RNN model consists of three components.
- First, RNN uses a region selector that performs a selective search to find pixels of region in each image. It merely extracts 2000 region proposals i.e bounding boxes
- Next, these bounding boxes are sent for image classification done by a series of convolutional operations
- Finally, each bounding box is classified as the name of the class it belongs
Yolo(You Only Looks One) Model
Yolo Model was discovered in 2015, In the previous object models, the model doesn’t look at the complete image instead it looks at the part of the image. But in the YOLO Algorithm networks looks one time before giving the output image. The entire extraction and classification are done in single networks. The Yolo algorithm is defined in detail in the article ”Yolo algorithm for object detection”.
Applications of Object Detection
Object detection technology is used in different ways through the implementation of computer vision and AI. It is really prevailing in our daily lives so let’s discuss how it is widely being used in our daily lives.
Autonomous Driving
Object detection is in autonomous driving. Self-driving cars use object detection to spot and locate objects like pedestrians, traffic signals, and other vehicles around them and identify the need of whether to turn the accelerator, brake, or move the vehicle making the flow of the journey streamlined and reaching the destination safely and efficiently.
Face Detection
Object detection models have used face detection, to detect faces, emotions, or behavior of a person, which is widely used in schools for automated attendance, Banks for detection of employee faces ensuring KYC, and you unlocking your phone through face detection. It is also used in person detecting or counting crowds in areas like malls, shops, parks, etc.
Retail
Object detection in retail is used very smartly keeping the manual retailing system aside and introducing automatic flow by tracking objects monitoring which item is picked by whom, having a check-in of shelf space, check-in of inventory stock, and gathering customer experience and satisfaction.
Ball Tracking in sports
Object detection models are used in sports like football, cricket, table tennis, etc to track and detect the movement of the ball. It provides coaches and analysts with a deep analysis of the sports event i.e tracking of ball and the movement of players which is much more difficult and time-consuming in traditional tracking methods.
Security and Surveillance
CCTV and surveillance cameras use object detection technology to track and detect the movement of objects in a particular scene or video. They are built using object detection models that are capable of monitoring and detecting multiple events that happen at once in a video frame. For example, theft detection, identifying criminals, and locating people in dangerous areas. The art of object detection helps to monitor these objects efficiently.
Medical & Healthcare
Object detection is deeply emerging in the field of medicine. In the medical field, most diseases are diagnosed by using images, scans, X-rays, and Photographs object detection can be used to objects in images like detecting tumors, and identifying cancerous cells. It is highly reducing the time spent on the manual process of CT Scans, and MRIs.
End Notes
That’s all folks, In this article, you have understood the basic concept of what is object detection, and how it works in traditional machine learning, and deep learning through convolutional networks. We have also discussed some object detection models and algorithms like YOLO & RNN Models in object detection along with some of the use cases of object detection.
