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<\/a><\/span><\/div><\/div><\/div><\/div><\/div>\nINDUSTRIAL AI: HOW DEEP LEARNING COMPLEMENTS MACHINE VISION SYSTEMS<\/h1><\/div>Within the realm of industrial automation, artificial intelligence (AI) has become quite the buzzword. Hype aside, AI has carved out a niche in the industrial space, offering tremendous value on the plant floor when properly designed and implemented. Deep learning \u2014 a subset of AI that uses neural networks such as convolutional neural networks (CNNs) that mimic the learning process of the human brain \u2014 has emerged as a popular industrial AI tool for its ability to aid in subjective inspection decisions and in inspecting scenes where identifying specific features proves difficult due to high variability or complexity.\u00a0<\/span><\/p>\nWhen it comes to choosing a deep learning solution, manufacturers have several options, including programming a solution in-house, leveraging establish frameworks such as PyTorch or TensorFlow. Manufacturers also have the option of purchasing an off-the-shelf solution or opting for an application-specific AI-enabled system or product.\u00a0<\/span><\/p>\nDeep learning, however, is not a standalone technology. Rather, when deployed alongside traditional machine vision<\/a> techniques, it becomes a powerful tool for automated inspection that enhances the overall capabilities of industrial automation systems deployed in applications including electronics and semiconductor inspection, consumer packaged goods inspection and automotive inspection. <\/span><\/p>\n<\/div><\/div><\/div>Applications<\/h6><\/div>- <\/i><\/span>\n
Semiconductor\/electronics inspection<\/p>\n<\/div><\/li>
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Weld inspection<\/p>\n<\/div><\/li>
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Medical\/pharmaceutical inspection<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
Bin picking \/ robotic pick-and-place<\/p>\n<\/div><\/li>
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Automotive inspection<\/p>\n<\/div><\/li><\/ul><\/div><\/div>
![\"ai](\"https:\/\/emergentvisiontec.com\/wp-content\/uploads\/2023\/11\/deep-learning.jpeg\")
<\/span><\/li><\/ul><\/div><\/div><\/div><\/div>DEEP LEARNING: A TOOL FOR THE MACHINE VISION TOOLBOX<\/h2><\/div>Machine vision refers to a set of hardware \u2014 including camera<\/a>s, optics, and lighting \u2014 that, along with the appropriate software<\/a>, helps businesses execute certain functions based on the image analysis done by the machine vision system. The four major categories of machine vision applications are inspection, identification, guidance, and gauging, and at the heart of all systems is the software, which helps drive component capability, reliability, and usability.<\/p>\nTraditional machine vision software refers to discrete, rules-based algorithms capable of a significant amount of automated imaging tasks. Examples of general machine vision algorithms include edge detection, image transformation, content statistics, correlation, geometric search, OCR\/OCV, and correlation. When it comes to incorporating deep learning into a machine vision system, it\u2019s important to remember that deep learning cannot be deployed into all machine vision applications.<\/p>\n<\/div>
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Within the realm of industrial automation, artificial intelligence (AI) has become quite the buzzword. Hype aside, AI has carved out a niche in the industrial space, offering tremendous value on the plant floor when properly designed and implemented. Deep learning \u2014 a subset of AI that uses neural networks such as convolutional neural networks (CNNs) that mimic the learning process of the human brain \u2014 has emerged as a popular industrial AI tool for its ability to aid in subjective inspection decisions and in inspecting scenes where identifying specific features proves difficult due to high variability or complexity.\u00a0<\/span><\/p>\n When it comes to choosing a deep learning solution, manufacturers have several options, including programming a solution in-house, leveraging establish frameworks such as PyTorch or TensorFlow. Manufacturers also have the option of purchasing an off-the-shelf solution or opting for an application-specific AI-enabled system or product.\u00a0<\/span><\/p>\n Deep learning, however, is not a standalone technology. Rather, when deployed alongside traditional machine vision<\/a> techniques, it becomes a powerful tool for automated inspection that enhances the overall capabilities of industrial automation systems deployed in applications including electronics and semiconductor inspection, consumer packaged goods inspection and automotive inspection. <\/span><\/p>\n<\/div><\/div><\/div> Semiconductor\/electronics inspection<\/p>\n<\/div><\/li> Weld inspection<\/p>\n<\/div><\/li> Medical\/pharmaceutical inspection<\/p>\n<\/div><\/li> Bin picking \/ robotic pick-and-place<\/p>\n<\/div><\/li> Automotive inspection<\/p>\n<\/div><\/li><\/ul><\/div><\/div> Machine vision refers to a set of hardware \u2014 including camera<\/a>s, optics, and lighting \u2014 that, along with the appropriate software<\/a>, helps businesses execute certain functions based on the image analysis done by the machine vision system. The four major categories of machine vision applications are inspection, identification, guidance, and gauging, and at the heart of all systems is the software, which helps drive component capability, reliability, and usability.<\/p>\n Traditional machine vision software refers to discrete, rules-based algorithms capable of a significant amount of automated imaging tasks. Examples of general machine vision algorithms include edge detection, image transformation, content statistics, correlation, geometric search, OCR\/OCV, and correlation. When it comes to incorporating deep learning into a machine vision system, it\u2019s important to remember that deep learning cannot be deployed into all machine vision applications.<\/p>\n<\/div>Applications<\/h6><\/div>
<\/span><\/li><\/ul><\/div><\/div><\/div><\/div>DEEP LEARNING: A TOOL FOR THE MACHINE VISION TOOLBOX<\/h2><\/div>