Impact of artificial intelligence and deep learning on machine vision

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this area includes robotics, language recognition, image recognition, Natural language processing and expert systems. Since the birth of artificial intelligence, the theory and technology have become more and more mature, and the application fields have also been continuously expanded. The application of artificial intelligence to different disciplines has greatly accelerated the development speed and adaptability of these disciplines. Applying AI to the machine vision industry will enable machine vision to surpass existing solutions and be more competent for more challenging applications. But is AI machine vision ready for industrial applications?

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The applicability of AI in machine vision depends on machine learning technology, and more accurately, deep learning capabilities. At its broadest level, AI can be defined as the ability of computers to simulate human intelligence, and machine learning enables computers to operate without explicit programming. Deep learning is a subfield of machine learning that enables computers to continuously learn from experience.

Some developments over the past decade have made it possible to apply deep learning techniques in machine vision. Based on the new technology of neural network, the graphics processing unit (GPU) has enough computing power and rich data. Now we can use artificial intelligence for image processing.

Deep learning brings hope to traditional machine vision technology because it is different from traditional image processing software that uses rule-based methods. At present, machine vision users can already find deep learning system software on the market. Compared with traditional machine vision solutions, another advantage of deep learning is that it can reduce the time required to develop machine vision programs.

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Deep learning also brings hope to some applications that face challenges using traditional vision systems, such as organic food inspection, seed sorting, etc. With the rise of AI in machine vision, this technology will be suitable for more inspection tasks, and eventually beyond the field of industrial automation. We have reason to believe that deep learning will have good prospects in the medical, life sciences, food, counterfeit inspection and wood grading industries.

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In the future, deep learning technologies will also be introduced in areas such as medical diagnosis, surveillance, autonomous driving, and smart agriculture to implement functions such as inspection or map analysis. AI is the future development trend, and it will soon help people solve some complex tasks, because computing power doubles almost every year.

Many machine vision professionals have recognized that AI and deep learning will have a significant impact on the vision industry, but they believe that the full potential of AI may not explode until at least 3 to 5 years. In addition, AI is not the only way to solve all traditional machine vision and image processing problems.

AI systems have two major disadvantages. First, you need a lot of training and you need to create a team of experts in order to reach the next level of classification. The second disadvantage is that once the training is completed and the classification fails, it is difficult to solve this problem. You have no choice but to train a new sample. Artificial intelligence is becoming more and more common in machine vision, and industry experts suggest that there should be focused development according to the situation of the enterprise itself.