AI Object Detection: State-of-the-Art

Introduction:

Object Detection is one of the most important and useful branches of computer vision. It is used in identifying and describing the contents of an image along with the location of these respective objects using bounding boxes. Here we will discuss about AI Object Detection using Deep Neural Networks

What is Deep Neural Networks(DNN) and How Does it Work?

Deep Neural Networks is a type of machine learning algorithm that has been developed in recent years. It is a technique that allows the AI to learn from data and identify patterns.

They are neural networks with many hidden layers between the input and output layers. DNNs are commonly used to classify different types of data, such as images or text.

The process of deep learning involves training a neural network on an input dataset, typically consisting of many examples, and then testing it on an output dataset, usually another set of examples from the same category as the input set.

How Does Object Detection Work in Computer Vision?

Computer vision is the study of machines that can understand what they see. It has been a hot topic in recent years, with many companies and researchers trying to build a computer that can do everything a human can. Object detection is one of the most important parts of computer vision because it allows computers to understand what they are seeing.

In this section, we will talk about how object detection works in computer vision, and how you can use Python to detect objects in images using OpenCV.

OpenCV in Python

OpenCV is a library of programming functions for real-time computer vision. It is used for developing high-level vision applications like object detection and recognition, image stitching, and 3D reconstruction.

It is an open source library that lets programmers use OpenCV from within the Python programming language.

The OpenCV module can be used to build a variety of applications, including face recognition, detection of pedestrians in videos, or finding the location of an object in an image.

How can Deep Neural Networks Help with Object Detection to Ensure the Accuracy of Results?

Deep Neural Networks in Artificial Intelligence allows for the object detection and identification to be more accurate. They analyze the image and then identify what is in it.

The accuracy of Deep Neural Networks is very high. It can detect objects that are very similar to one another with a higher degree of accuracy than humans can. This is because they have been trained on many different types of images, which allows them to be more familiar with what they should be looking for in an image.

Now lets see and example of Object Detection using OpenCV,

This program was drafted by me and now lets see the working of Object Detection using OpenCV

What I have used:

Running inference on pre-trained deep learning models from well-known frameworks like Caffe, Torch, and TensorFlow is supported by the OpenCV DNN module.

Popular detection frameworks for object detection include

1. YOLO

2. SSD 

3. Faster R-CNN

OpenCV's DNN module now supports the operation of YOLO/DarkNet.

YOLO (You Only Look Once)

Download the pre-trained YOLO v3 weights file from my GitHub profile and place it in the current directory or you can directly download to the current directory in terminal using

$ wget https://pjreddie.com/media/files/yolov3.weights

Provided all the files are in the current directory, below command will apply object detection on the input image dog.jpg.

You can get the source code here : https://github.com/SHariVigneshwaran/SHariVigneshwaran-Object-Detection-YOLO-OpenCV.git

To run this code use this command : $ python yolo_opencv.py --image dog.jpg --config yolov3.cfg --weights yolov3.weights --classes yolov3.txt

Input Image:

dog.jpg

Output image:

object-detection.jpg

Conclusion

Computer Vision has been a major focus for Artificial Intelligence research in recent years with many applications such as Object Detection and Image Recognition. Deep Neural Networks have proven to be very successful at solving these problems with their ability.