Object Detection

What is Object Detection??

Object detection is a computer vision technique, for locating the objects in an image or a video. Object detection is one of the fastest growing technology. As we can see the self-driving cars is a result of the growing industry! Self driving cars also uses the technique of object detection to drive, such as: detecting driving lanes or pedestrian detection to improve road safety. Basically, it is a technique where a computer uses certain algorithms to detect the object and can identify the particular object from the image or video.

Basic understanding of Deep learning in Object Detection !!

CNN is the basic algorithm which uses object detection. let us deeply understand this!

A neural network consists of 3 layers:

1. Input layer
2. Hidden layer
3. Output layer

In an Input layer, we input the image, which extracts the features of an image, through connections and pooling layer. extracts features such as: color, shape, edge.

Hidden layers are considered as convolutional layers ,a filter is imposed on every layer such that for each layer there should not be a repeated input. extracts features such as: object presence.

example: 1st layer output will be fed to 2nd layer input and 2nd layer output is fed to 3rd layer as its input and so on….

In the output layer finally the object will be detected. This layer classify the object with an accurate label.

CNN Architecture is shown below:

How Object Detection works??

Object detection works uses various techniques to perform Object detection, Deep learning approaches: such as CNN,R-CNN, YOLO which can automatically detect the objects!

You can choose from two-key approaches to get started:

1. Create and train the custom object detector: In this, we manually set up the layers and weights in the CNN.

2. Use a pre-trained object detector: This may provide faster results than we assigning weights because object detectors have already been trained on thousands or even more images.

There are two types of network stages:

1. Single stage networks: In Single stage networks, we use YOLO v2, CNN which produces network prediction around entire image using anchor boxes. Single stage network can be much faster but they may not recah the accuracy!.

1. Two-stage networks: In two-stage networks, we use R-CNN and its variants, in which only a specific object (subset) of an image will be identified and classify it within the region proposals. These are slow network, but accuracy can be achieved!.

Some of the algorithms used are:

1. CNN (Convolutional Neural Network).
2. RCNN (Region Convolutional Neural Network).
3. Fast R-CNN.
4. Faster R-CNN.
5. Mask R-CNN.
6. YOLO.

This was an basic introduction regarding object detection.

That’s it for this week!

Well, I hope you gain interest in reading the article about object detection.