Entrepreneur, technology evangelist and business strategist. President and cofounder of Visartech Inc., a software solution provider.

Many of today’s businesses have recognized the benefits of AI. McKinsey reports that computer vision ranks second among all other AI solutions in terms of application, and Statista research predicts that the computer vision market will expand with an annual growth rate of 11.69% and reach $50.97 billion by 2030. This growth is possible thanks to advancements in deep learning architectures, better machine learning methods, modern custom hardware accelerators and the advent of 5G technology.

Three Operations Executed By Computer Vision In Industrial Settings

AI vision is not only about automation. A well-developed computer vision system adjusted to specific business requirements can solve different problems industrial companies may face.

Converts 2D Images Into 3D Models And Verifies Designs

3D models allow companies to view production assets with depth, but their creation requires manual labor and much time. AI vision’s role is to accelerate the conversion of computer-aided design (CAD) files to 3D models. To do this, you’ll need to create and use advanced techniques and algorithms.

One such technique is structure from motion (SfM), which estimates spatial relationships between different points to create a 3D model. Another important method is stereo vision, which analyzes pairs of images taken from slightly different viewpoints to make a depth map.

Imagine a scenario where an oil and gas enterprise is planning to explore a new oilfield. By using computer vision techniques, the company can transform 2D satellite images of the area into intricate 3D models faster and with better accuracy.

Guides Product Assembly And Management Processes

Ensuring compliance with various requirements during the assembly of products, particularly complex ones such as heavy machinery components, poses a significant challenge. The incorporation of computer vision through cameras or sensors empowers companies to identify the slightest deviations from established standards.

You can also train your vision model to monitor and manage production processes. In this case, your AI software solution will track the movement of work elements in the factory, optimizing the location of equipment and resources to increase productivity.

For example, Belgian developer and supplier of glass-fiber products 3B-Fibreglass leverages computer vision to oversee the movement of fibers exiting a bushing. The AI ​​system determines where the production flow was disrupted or predicts where it might happen in an average of 75 seconds.

Steers Robots In Disassembly Workflows

Computer vision in robot-controlled dismantling relies on numerous sensors and cameras to gather detailed environmental data. This data enables precise object localization and awareness of surroundings, ensuring robots operate with accuracy and safety. This approach efficiently dismantles heavy structures, maintaining high safety standards and optimizing work processes.

For instance, underwater maintenance companies use robots to dismantle and replace individual components of marine structures. Object detection, position estimation and path planning algorithms can help robots avoid obstacles and maneuver efficiently in challenging environments. Neptune Robotics uses AI-powered robots to identify organic materials on the surfaces of ships and safely eliminate them without causing any harm to the paint.

Detects Technical Issues In The Field Operations

Drones equipped with computer vision technology can automatically inspect external equipment or the workplace and perform visual data analysis on the server side. If any anomalies are detected, the system can note the exact location of the breakdown. This avoids the cost of sending workers out to find the problem manually.

In the energy sector, such drones have been programmed to identify signs of wear and tear, corrosion or damage to power line infrastructure. By creating an algorithm for deep data analysis, you can also discover the nature of the failure, its possible causes, and recommendations for the necessary resources to fix the problem.

Computer Vision Implementation Challenges And How To Address Them

Opposite to the other domains, industrial enterprises require the use of more complex algorithms and techniques when working with visual data. Typically, it may lead to these challenges:

Developing Complex Algorithms Yet With Less Resources

Creating proprietary algorithms to identify and analyze objects in videos and photos demands significant financial investment and time commitment. Using AI vision for particular business scenarios adds complexity, necessitating testing and effectiveness assessment.

Nevertheless, addressing this challenge becomes feasible through the integration of artificial intelligence cloud platforms like Azure AI or AWS AI services. Leveraging these cloud solutions can enable businesses to expedite the testing and implementation of computer vision technologies.

Unstable Conditions For Data Gathering

Dust, darkness, high temperature or humidity are usual production conditions for industrial enterprises. However, they can lead to insufficient quality or inferiority of the received images and can complicate the accuracy of AI vision solutions’ analysis.

When facing such a challenge, you may apply automated labeling and correction of anomalies in data. The first method speeds up the data tagging process and reduces the likelihood of errors that may occur during manual labeling. With the second, you’ll save your time in identifying, rectifying or mitigating irregularities, outliers or unexpected patterns within datasets.

Data Preprocessing And Labeling

Companies face challenges in organizing visual data from diverse sources, including varied angles, weather conditions and production speeds. The large and heterogeneous nature of this data poses obstacles to accurate labeling.

In addition to implementing automation for data labeling, organizations can take several internal steps to ensure correct data labeling:

1. Develop comprehensive and clear guidelines for data labeling.

2. Implement robust quality control measures to regularly audit and validate the labeled data.

3. Keep labeling teams updated with regular training sessions on the latest labeling standards.

Considering and preparing for the various challenges associated with AI computer vision adoption is key. Doing so prepares business leaders for the ups, downs and tough decisions as AI application speeds up. Following mentioned solutions for effective implementation of AI vision solutions can ensure your investments bring the highest returns.

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