Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Table of contents
1.
Introduction
2.
Object Detection
3.
The R-CNN Models
3.1.
R-CNN 
3.1.1.
Drawbacks of R-CNN 
3.2.
Fast R-CNN
3.2.1.
Drawbacks of Fast R-CNN
3.3.
Faster R-CNN 
3.3.1.
Drawbacks of Faster R-CNN
4.
Object Detection models
5.
Frequently Asked Questions
5.1.
What is CNN object detection?
5.2.
What is the difference between object recognition and object detection?
5.3.
What is hog object detection?
5.4.
How do you learn object detection?
6.
Conclusion
Last Updated: Mar 27, 2024

Object Detection Using Deep Learning

Author Tashmit
1 upvote

Introduction

How many times have you lost the TV remote, and how much time did you spend finding it? It happens to most of us, and it is the most frustrating experience. However, if I told you a computer algorithm could solve the problem in a few milliseconds?

Object Detection is a method to solve such problems. In this article, we will deeply understand object detection.

Object Detection

Object Detection is a robust deep learning algorithm that can locate the presence of an object in a boundary box.

Source: Link

The model's performance is evaluated using the mean classification error across the predicted class. The arrangement for the single-object localization model is estimated using the distance between the expected and predicted boundary box. 

Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

The R-CNN Models

The Region-based Convolution Neural Network Models consist of three categories in which object detection can be done. 

R-CNN 

The RCNN algorithm puts multiple boxes in the image and checks if any box contains any object. It applies selective search to extract the package from an image. 

Source: Link

The R-CNN algorithm first takes an image as input and applies the AlexNet Convolution. Then the object is extracted, and with the help of SVM, the thing is classified. 

Drawbacks of R-CNN 

Although it was the first algorithm used for object detection, it is prolonged. The CNN used for feature extraction, SVM applied for identifying objects, and Regression models for tightening the box boundary take about 50-60 seconds for each image. It is practically impossible to use this algorithm when dealing with a vast dataset. 

Fast R-CNN

To reduce R-CNN's time, we run the CNN only once for each image and get the objects present in a snap.

Source: Link

We send the input image through a deep convolution neural network used for feature extraction. At the end of the CNN layer, a custom layer is known as Region of Interest Pooling or RoI Pooling. It is responsible for extracting specific features for a given input region. A fully connected layer then interprets the output of the CNN then the model divides into two works, one for the class prediction and another with a linear result for the bounding box. This process is repeated multiple times for each region of interest in a given image.

Drawbacks of Fast R-CNN

Even though Fast R-CNN uses a selective search method to discover an RoI layer, it is slow. It takes around 4-6 seconds to detect an object for each image which is time-consuming for a large dataset.

Faster R-CNN 

The Faster R-CNN algorithm is a modified version of the fast R-CNN algorithm. The only difference is that Fast R-CNN applies selective search for generating Regions of Interest, whereas the Faster R-CNN algorithm uses Region Proposal Network.

Source: Link

The Region Proposal Network takes the output of a pre-trained deep CNN model. It passes a small network over a feature map that generates multiple region proposals, each with an objectness score as output.

Drawbacks of Faster R-CNN

The algorithm doesn't look at the image as a whole but looks for objects region-wise; therefore, it requires many passes through a single image to extract everything. Also, different systems are working one after the other; the performance of the methods further ahead depends on how the previous procedures were performed.

Object Detection models

Also read, Sampling and Quantization

Frequently Asked Questions

What is CNN object detection?

Object detection consists of two tasks that are classification and localization. R-CNN stands for Region-based Convolutional Neural Network. 

What is the difference between object recognition and object detection?

Object detection is the process of finding instances of objects in images. In deep learning, object detection is a subset of object recognition, where the thing is not only identified but also located in a snap. It allows multiple objects to be identified and located within the same idea.

What is hog object detection?

Histogram of Oriented Gradients, also known as HOG, is a feature descriptor like the Canny Edge Detector, SIFT (Scale Invariant, and Feature Transform). It is used in computer vision and image processing for object detection.

How do you learn object detection?

Object detection can be done by machine learning or a deep learning approach. The machine learning approach requires the features to be defined using various methods and then any classification technique.

Conclusion

In this article, we have extensively discussed Object Detection and its various types of algorithms. We hope that this blog has helped you enhance your knowledge regarding object detection .

Refer to our Guided Path on Coding Ninjas Studio to upskill yourself in Data Structures and AlgorithmsCompetitive ProgrammingJavaScriptSystem DesignMachine learning and many more! If you want to test your competency in coding, you may check out the mock test series and participate in the contests hosted on Coding Ninjas Studio! But if you have just started your learning process and are looking for questions asked by tech giants like Amazon, Microsoft, Uber, etc; you must look at the problems, interview experiences, and interview bundle for placement preparations.

Nevertheless, you may consider our paid courses to give your career an edge over others!

Do upvote our blogs if you find them helpful and engaging!

Happy Learning!

Previous article
Homography
Next article
Human Detection in Computer Vision
Live masterclass