One of the most talked-about buzzwords of late is “deep learning,” which is an area of machine learning that enables computers to be trained and learn. Deep learning-which can be accomplished through architectures such as artificial neural networks-imitates the way the human brain works by processing data and creating patterns for use in decision making.
Major companies such as Google, Facebook, IBM, Intel, and Microsoft have made recent headlines regarding their involvement in the deep learning space but off late the trend is also moving towards Machine vison in the field of automation.
The application of software development that uses artificial intelligence to improve image analysis in applications where it is difficult to predict the full range of image variations was the beginning of this era entering in to deep learning.
There are few companies in the world that entered this market with their Deep learning software exclusively to address complex problems of classification which were difficult or impossible using conventional machine vision algorithms.
Deep learning is a type of learning by Machine in which a model created learns to perform classification tasks directly from images in Vision. Deep learning is usually implemented using a neuralnetwork architecture with more number of layers in the network and hence called as deep learning. Unlike traditional neural networks contain only 2 or 3 layers, deep networks can have hundreds.
A deep neural network combines multiple nonlinear processing layers, using simple elements operating in parallel and inspired by biological nervous systems. It consists of an input layer, several hidden layers, and an output layer. The layers are interconnected via nodes, or neurons, with each hidden layer using the output of the previous layer as its input.
Deep learning often requires hundreds of thousands or millions of images for the best results. It’s also computationally intensive and requires a high-performance GPU.
Machine Vision based factory automation is one area that has embraced “Deep Learning” to play a role in classification, segmentation and recognition where normal conventional machine vision algorithms fail.
Deep learning-based algorithms provide higher inspection accuracy than that found in visual inspection and conventional machine vision inspection. With high-accuracy automated inspection capacity, it is possible for one person to manage more than one inspection equipment.
By using GPU-processing languages, such as CUDA and CUDNN, deep learning algorithms with support for Multi-GPU and Multi-Threading, the image processing speed can be maximized.
Not every application benefits from deep learning capabilities. Good candidates for deep learning then, may be inspection applications where there is no pre-defined shape. Deep learning and machine vision make a powerful combination. Deep learning enhances machine vision capabilities and opens up entirely new application possibilities. The two technologies are only just beginning to merge and have the potential to revolutionize the way machine vision is deployed in nearly every way.
Online Solutions represents SUALAB Korea for their Deep learning software SUAKIT in India.
SuaKIT is a deep learning machine vision inspection software that can be used in various manufacturing fields such as display, solar, PCB, film, and semiconductor. With SuaKIT’s deep learning algorithms, the limitations of traditional machine vision inspection methods can be overcome.
SuaKIT Core Functions
Segmentation- Inspect defect areas by analyzing various defects on the products.
Classification- Classify images and categorize them into defect types or OK/NG groups.
Detection – Detect each target object in an image by class.
SuaKIT Training Methods
Single Image Analysis – Train and inspect features of each image
Image Comparison – Train and inspect defects by focusing on the differences between two images
Multi Image Analysis – Train and inspect defects by analyzing the correlations among various imagesOne Class Learning- Inspect defects by training only normal images (Without defect images)
Deep Learning software in India – usage is comparatively expensive and may take some more time to enter the Machine vision market of India in factory automation. With the boost to export in the country, more quality products and Industry 4.0 drive, the deep learning software would find an edge in coming years.
Research and development centres should invest on this deep learning software in India for learning and also to come out with advanced practical solutions to the field of automation and Machine Vision.
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