Insights from 100+ conversations on industrial computer Vision in Manufacturing

Manufacturers are always looking for ways to improve their processes and stay ahead of the curve [1]. One of the most promising AI solutions that is transforming the manufacturing industry is Computer Vision. It is a field of Artificial Intelligence that enables computers to obtain structured and meaningful information from digital images, videos, and other visuals. Based on this information, actions or recommendations can be given. In the context of Industry 4.0, computer vision AI enables manufacturers to improve operational efficiency, quality control processes, predictive maintenance, and workforce safety measures [2]. It enables machines to "see" and understand the production environment, allowing for real-time monitoring, automation, and optimization.

?Deploying computer vision models in production is a complex endeavor that requires a holistic approach that encompasses data, models, infrastructure, and processes. By addressing the challenges of data acquisition, model selection, infrastructure, CI/CD, monitoring, and ethical considerations, organizations can successfully deploy computer vision models at scale [2].

So why the surging interest?

Recent technological advancements in computer vision and machine learning have opened up a world of possibilities for industrial applications. The manufacturing industry is currently undergoing a massive transformation referred to as the fourth industrial revolution (4IR), headlined by the adoption of computer vision, artificial intelligence, robotics, and the industrial internet of things (IIoT) [3]. 4IR technologies present an estimated multi-trillion dollar opportunity and will enable factories to operate more accurately, efficiently, and safely [3].

GPUs have played a crucial role in the development of deep learning and computer vision. They are capable of processing large amounts of data in parallel, making them ideal for training deep neural networks. NVIDIA’s A100 GPUs, for example, have been shown to improve computer vision performance by up to 10x compared to previous generations [4]. The fusion of computer vision with deep learning, along with advances in graphics processors that run many calculations in parallel and the availability of huge data sets, has led to leaps in accuracy [5]. The traditional machine learning flow involved feeding the system image data (input), requiring manual extraction of features and labeling of these features. Deep learning, on the other hand, uses neural networks to learn features from the data itself, making it possible to train models with large amounts of data and achieve high accuracy [6].

In conclusion, the combination of deep learning and GPUs has enabled significant advancements in computer vision, making it possible to train models with large amounts of data and achieve high accuracy. These advancements have opened up a world of possibilities for industrial applications, enabling factories to operate more accurately, efficiently, and safely.

Traditional video analytics vs AI video analytics: What’s the difference?

AI video analytics leverages artificial intelligence technologies, including machine learning and deep learning, to intelligently analyze video streams. With the ability to learn from data, adapt, and improve over time, AI video analytics excels in object tracking, handling complex scenarios, and performing advanced tasks like facial recognition and behavioral analysis. It provides context-aware and situationally dependent insights, outperforming traditional methods.

Advantages of AI video analytics:

• Context-aware and situationally dependent tracking.
• Simultaneous monitoring of multiple objects.
• Handling complex scenarios like occlusions.
• Continuous improvement through learning from past data.
• Advanced tasks like object detection, facial recognition, and behavioral analysis.

How can AI video analytics benefit your business?

Widely adopted across industries for valuable insights and improved security. Traditional and AI video analytics find applications based on specific needs. AI video analytics excels in object recognition, dynamic decision-making, and adaptability to complex environments.

Key themes from various conversations

1. Predictive Maintenance: AI is able to analyse data and predict when a piece of equipment might fail. Through the monitoring of factors like temperature, vibration and performance metrics, AI can anticipate maintenance needs, allowing technicians to perform maintenance before any breakdown occurs. This ultimately minimises any downtime and prevents costly unplanned production delays.
2. Assembly Inspection: AI can be used for assembly inspection to detect missing parts, wrong product or asset configurations, and the correct assembly order during various production stages. By analysing visual data, AI-powered systems can compare the assembly process with predefined guidelines or CAD models to identify deviations or errors. This helps ensure that products are assembled correctly, reducing the chances of faulty or non-compliant products reaching the market.
3. Defect Detection and Classification: AI-powered systems can analyse visual data to identify defects in products or components with precision. The key to a successful deployment is to require as little data as possible to get started. Modern AI approaches, such as few-shot learning, have made it possible to train models with only a small number of samples, reducing the burden of data collection and annotation. By training algorithms, the AI models are able to recognise patterns associated with defects and classify them accurately. This automated defect detection enhances quality control processes, reduces reliance on manual inspections, and ensures consistent and reliable results.
4. Surface Inspection and Texture Analysis: AI algorithms excel at inspecting surfaces for imperfections, scratches, or other irregularities. By analysing visual data, AI-powered systems can identify and classify surface defects or variations, even in complex textures. This is crucial in industries such as automotive, electronics, and textiles, where surface quality directly impacts the performance and aesthetics of products.
5. Object Counting and Dimension Measurement: AI algorithms can automatically count items and measure the dimensions of objects of interest within images or videos. This capability is useful in inventory management, logistics, and quality control scenarios.
6. Quality control in food preparation: In the food industry, AI plays a pivotal role in ensuring quality across various stages. It can identify overripe fruits in the vegetable preparation area, triggering automated rejection. Additionally, in the cooking section, AI can verify ingredient additions to recipes, preventing substandard products. As the process progresses to the packaging stage, AI-powered cameras meticulously examine final products, ensuring the accuracy of labels, expiry dates, and overall packaging integrity.

These key themes collectively illustrate the diverse applications of computer vision in industrial settings, ranging from assembly inspection to quality control in food preparation. Each theme showcases how AI technologies enhance efficiency, accuracy, and reliability in different aspects of manufacturing and production processes.

Crosscutting themes in computer vision for industrial applications:

• Decisive data-driven strategies: Empowers decision-makers with data-driven insights, supporting informed decision-making for process optimization. Forms a foundation for strategic planning and resource allocation based on real-time analytics.
• Holistic operational enhancement: Synergizes operational efficiency improvement by optimizing processes, automating tasks, and providing real-time insights. Aligns with quality control, predictive maintenance, and safety measures for a comprehensive operational upgrade.
• Smart quality assurance: Combines quality control enhancement with innovative defect detection and automated visual inspection. Ensures a meticulous approach to product quality, reducing defects and inconsistencies in manufacturing.
• Proactive maintenance and safety integration: Integrates predictive maintenance practices with workforce safety measures to ensure a holistic approach. Utilizes computer vision for identifying potential machinery issues and maintaining a safe industrial environment.
• Agile real-time monitoring: Incorporates real-time monitoring and alerts into various applications, enhancing responsiveness and facilitating immediate corrective actions. Acts as a common thread in automation, safety, and quality control for dynamic and adaptive industrial processes.
• Versatile automation solutions: Advances automation in manufacturing through computer vision integration, impacting tasks like robotic guidance and material handling. Demonstrates adaptability and versatility by addressing diverse automation needs across industries.
• Industry 4.0 integration: Aligns with Industry 4.0 concepts by fostering digital integration and creating intelligent, connected industrial ecosystems. Drives the fourth industrial revolution through the adoption of data-driven and interconnected technologies.
• Cross-industry adaptability: Exhibits cross-industry adaptability, tailoring computer vision solutions to unique challenges in automotive, healthcare, electronics, and more. Demonstrates the universal applicability of computer vision across diverse industrial sectors.

These crosscutting themes underscore the comprehensive and transformative nature of computer vision in industrial applications, where its impact extends across efficiency, quality, safety, automation, and global technological trends.

From conversation to action

For manufacturers who are still hesitant about adopting this technology, it’s crucial to recognize that the future of manufacturing will undoubtedly be digital. Those who invest early and strategically in computer vision will be better positioned to capitalize on its benefits, ensuring a competitive edge in a rapidly evolving market.

Computer vision represents a wonderful opportunity for manufacturers to not only streamline their operations but also to reimagine the boundaries of what’s possible in the realm of modern production. As the digital era progresses, the integration of such technologies will be indispensable for manufacturers aiming to remain at the forefront of their industry.

Some of the factors you should consider when selecting a video analytics software for your company:

1. Can it provide long-term cost savings?
2. Is it user-friendly for non-technical users?
3. Can it be customized for specific business needs?
4. Does it prioritize data privacy and compliance?
5. Is it scalable to accommodate business growth?
6. Can it integrate seamlessly with existing systems?
7. Does it offer advanced search capabilities?
8. Is it constantly updated to stay at the forefront of technology?
9. Does it demonstrate high reliability and performance?