AI and Artificial Vision: What’s the Difference?
In today’s rapidly evolving technological landscape, artificial intelligence (AI) and artificial vision are becoming central to a variety of industries, particularly in the automotive sector. These two fields, though interconnected, serve different purposes in streamlining processes and enhancing productivity. The role of AI as the brain behind the decision-making process and artificial vision as its eyes is a useful analogy for understanding how these technologies work together. Let’s explore the distinctions, applications, and future potential of these technologies, particularly focusing on the automotive and manufacturing sectors.
The Rise of AI in Data-Driven Industries
Over the past decade, we have seen an explosion in data generation and utilization across industries. In 2020, businesses handled volumes of data that far surpassed those of the 1990s. For instance, companies that managed 100 MB of data monthly in the early 90s are now processing petabytes of data, equivalent to 10 million times more. This staggering growth has brought about the need for AI to manage and process these immense datasets efficiently.
According to Seagate and IDC’s “Rethink Data” report, enterprise data volumes were projected to grow at an annual rate of 42.2% between 2020 and 2022. By the end of 2024 companies are projected to manage more than 97 ZB of data by 2024, in global terms, with the vast majority being unstructured data.
How can we manage all this data and make it usable? That is where AI appears and make great difference. This new trending tool (even though that it was first mentioned in 1956 by John McCarthy) is set to have a major impact on our future technology and big data development.
Then, the question is about whether AI would quit our future jobs or about how can we make better decisions on all our task responsibilities (that can be applied to every field and level). The answer seems obvious and that’s the reason why there is more and more companies emerging to be part of the change revolution.
Artificial Vision vs. AI: Understanding the Key Difference
While AI focuses on learning from data to make decisions, artificial vision deals with interpreting and understanding visual information from the physical world. Artificial vision, also referred to as computer vision, enables machines to “see” and interpret images or videos. In industrial settings, this technology plays a crucial role in automating tasks like quality inspection, monitoring, and object detection.
A key distinction between AI and artificial vision lies in their operational focus. AI encompasses a broad range of functions including natural language processing, machine learning, and predictive analytics, while artificial vision is specifically concerned with visual data interpretation. In the automotive sector, artificial vision systems are used to inspect vehicle components such as head, rear lamps or interior lighting, ensuring they meet quality standards before they are assembled.
The combination of AI with artificial vision has taken quality inspection to a new level. Traditional vision systems relied on static algorithms to detect defects, but AI has introduced adaptive learning into the process. By employing machine learning (ML), vision systems can now “learn” from past inspections, becoming more accurate over time.
For instance, AI models can detect subtle deviations that might indicate a manufacturing defect, improving the overall quality of the final product.
In the automotive industry, this capability is crucial for maintaining consistency across large production volumes. AI-powered vision systems can inspect thousands of parts per hour with a level of precision that manual inspection could never achieve. This leads to significant cost savings by reducing rework, waste, and production delays.
The Importance of Image Acquisition and Quality
An often-overlooked aspect of artificial vision systems is the importance of image acquisition and image quality. The effectiveness of an artificial vision system is directly related to the quality of the images it processes. High-resolution cameras, consistent lighting, and minimal noise are essential to capturing clear and detailed images that the vision system can analyse. Poor image quality, whether due to low resolution or unfavourable lighting conditions, can lead to incorrect conclusions, undermining the system’s reliability. That is where OIT® systems make the difference (reach out www.isr.es to check out ISR solutions).
In industrial settings, maintaining optimal conditions for image acquisition is crucial. The integration of specialized lighting systems and advanced sensors ensures that even in challenging environments, such as those with reflective surfaces, vision systems can capture high-quality data. Specular Vision is designed to handle these complexities by compensating for reflections and ensuring that the system captures the necessary details to make accurate assessments.
One notable development in the field is Specular Vision, a specialized vertical of artificial vision technology that focuses on inspecting reflective or specular surfaces. For example, in the automotive industry, reflective surfaces such as plastic injection, glass components, car paint or metallic parts present challenges for traditional vision systems, as they reflect light in ways that can obscure defects. Specular Vision uses advanced algorithms to detect imperfections on these reflective surfaces that even the human eye might miss, ensuring a higher level of quality assurance.
This type of technology not only reduces human error but also enhances production efficiency by identifying defects earlier in the process, thus preventing defective parts from advancing down the production line. Moreover, this technology can work continuously without the need for breaks, further improving throughput in factories.
The Future of AI and Artificial Vision in Manufacturing Industry
The integration of AI and artificial vision in quality control in the manufacturing sector is still in its early stages, but its potential is immense. As production lines become more automated, these technologies will play an even greater role in ensuring that vehicles meet the highest quality standards. In the near future, it is conceivable that entire production lines could be monitored and controlled by AI-driven vision systems, further reducing the need for human intervention. Looking ahead to 2030, it is difficult to imagine a car factory without AI-powered vision systems. The ability of these systems to operate at scale, combined with their precision and adaptability, makes them indispensable tools for the future of manufacturing. With the global AI market expected to reach $1.85 trillion by 2030, the automotive industry will continue to invest heavily in these technologies to remain competitive.
Conclusion: AI and Artificial Vision Working Together
Let’s sum up the article in the following points:
- AI and artificial vision are distinct but complementary technologies that are transforming industries, particularly in automotive manufacturing.
- Artificial vision systems, powered by AI, improve quality control by automating inspection tasks, reducing human error, and enhancing efficiency.
- Specular Vision technology is particularly useful for inspecting reflective and transparent surfaces, which are challenging for traditional systems.
- The future of key manufacturing sectors will heavily rely on AI-driven vision systems to meet the increasing demands for precision and automation.
Author: Juan Gómez
