Why use CNN for image recognition? Since recent advances contribute to the growth of image recognition. Such as AI and machine learning.
Then let us understand what is image recognition? Also, in various industries, how useful it is.
Meaning of Image Recognition
This is a software or systems ability. The ability to identify people, objects, actions in images, and places.
Also, it is using technologies of machine vision with AI. Yet, it has skilled algorithms to identify images by a camera system.
Image recognition has taken over the world. Due to the recent advances in machine learning. As well as the increase of the machine’s computational power.
The following may have an extensive application of image recognition:
- E-commerce
- Automotive
- Manufacturing Industries
- Retail
- Surveillance
- Security
- Farming
- Healthcare
Challenges
The following are the image recognition challenges we need to know.
- Viewpoint Variation
- Deformation
- Scale Variation
- In-class Variation
- Occlusion
Why Use CNN For Image Recognition
CNN plays an important role. Particularly in solving the problems said above.
CNN fuses changes in the operations mode. CNN inputs are not fed by complete numerical image values. Instead, into many small columns, they divided the complete image. And each set itself acts like an image. Also, the complete image is divided by a small size filter into small sections. Each neuron set is associated with the little section of the image.
Moreover, these images are treated. Like the process of a regular neural network. The computer is collecting the patterns regarding the image. And save the results in the matrix format.
This process is just repetitive. Until the complete image in the size of the pieces is distributed to the system. So a huge Matrix is a result. Representing the various patterns obtained by the system from the input image.
But this Matrix is ignored by a technique known as Max-Pooling. Because from each sub-matrix it retrieves the maximum values. And resulting in a smaller-sized matrix
These values represent the image pattern. This generated matrix is given to neural networks. While input and output refer to the probability of image classes.
Further during this training, you will identify different levels of features. Also, it has been label as, low, mid, and high-level.
The features of low-level include contrast, lines, and colors. While the feature of mid-level is identifying corners and edges. Whereas the high-level is identifying the class and specific sections.
Thus, CNN is reducing the power requirement in the calculation. Also, it is allowing the treatment of images with large size. Moreover, it is sensitive to image variations. So it can provide more accurate results than regular neural networks.
Image Recognition Uses
- Manufacturing
To minimize defects in the final product, monitoring quality is important.
- Drones
Provide image recognition abilities to Drones. So that it can provide automatic vision-based monitoring. As well as control and inspection of assets in remote areas.
- Autonomous Vehicles
Image recognition for Autonomous vehicles can identify road activities. Also, it takes the needed actions.
- Military Surveillance
Detecting uncommon activities in border areas is a big help. As well as automatic decision-making skills. Because it can prevent passing into and will result in saving the soldier’s lives.
