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Machine Vision vs AI Computer Vision – What should manufacturers choose?

Machine Vision vs AI Computer Vision – What should manufacturers choose?

What would the world be without manufacturing industries? The products we use in our daily lives; steel, fabric, plastic, pharmaceuticals, etc. are an outcome of an extensive manufacturing process. If there is a single glitch in the manufacturing process, the final product will lose its market standing; therefore, investing in technologies to monitor every stage of the manufacturing process is a necessity for every manufacturing industry today. 

Machine Vision System

While Machine Vision technology was popularly used among all the major manufacturing industries, the technology could not keep up with the pace of increased customer expectation for product quality, due to the following limitations: 

  • The technology used a golden template to detect surface defects and errors and was rigid towards image orientation; as a result, Machine Vision could only detect the obvious defects and could not capture the microscopic defects. 
  • Machine Vision requires the installation of expensive hardware and had limited functionality 
  • The training process and deployment cycles were time-consuming and costly 
  • Machine Vision technology was not flexible to the manufacturer’s change in requirements

Manufacturers of various industries now seek a technology, much stronger and better than a Machine Vision system to scale-up the overall manufacturing process.

AI-backed Computer Vision System

AI-backed Computer Vision System has begun to disrupt all the major manufacturing industries. Manufacturers who augment their factories with AI-backed Computer Vision System have experienced the following advantages: 

Quality Assurance 

The machine vision’s capability to capture microscopic defects is limited. Computer Vision takes multiple images of a product on full speed conveyors and effortlessly captures microscopic defects on various product surfaces irrespective of the image orientation, enabling the manufacturer to take corrective measures at the right time, enhancing the overall product quality. 

Reduction of Operational Costs

Here’s an instance: A steel manufacturer discovers that an entire batch of the product has minute chipped corners. The vision system he invested in failed to detect the chipped corners. Imagine the loss of time, money, material, and efforts the manufacturer will have to bear to produce another batch, all over again!  

AI Computer Vision detects errors at the right time and saves the organization from spending a fortune on remediating measures. According to an interesting article, with the help of machine learning, one of the esteemed company managed to save USD 1 billion in 2017!

Shorter Production Cycles

As production cycles get shorter, the production capacity becomes higher. When the production cycle is not bound to time and labour-intensive processes and is stringently monitored for defects and errors, the production cycle becomes faster and more efficient. Manufacturers can produce quality goods within a reasonable time frame and are ready to deliver them to the market just when the demand is at a peak. 

Improved Employee Productivity 

When manufacturers choose AI Computer Vision over the flawed Human Vision or Machine Vision, the manpower at the factory can be put to better use. While AI Computer Vision accurately inspects the product for quality assurance, the labourers are spared from this strenuous process and can be employed in other processes of manufacturing that require adequate human intervention. 

The Future of AI Computer Vision System

The AI in the computer vision market was valued at USD 2.37 billion in 2017 and is expected to reach USD 25.32 billion by 2023, at a CAGR of 47.54% during the forecast period. 2017 has been considered as the base year, and the forecast period is from 2018 to 2023.*

Lincode’s AI Computer Vision Solution 

Lincode’s AI Visual Inspection System is a state-of-the-art, Industry 4.0 solution that enables manufacturers to identify surface defects, identify and measure individual components, and generate real-time data about products on the production line. With AI and deep learning, we can significantly reduce false-positives and false-negatives generated by traditional machine vision and capture untrained defects.

Reach out to us at to know more about our solution. 
*source: |

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