Machine Vision in Industrial Automation

(Source: xiaoliangge- stock.adobe.com)

 Industrial automation is the use of technology and machinery to replace or augment human labor in manufacturing and industrial processes. It involves integrating sensors, control systems, robotics, and software to perform tasks with precision, speed, and consistency. Moreover, industrial automation aims to improve efficiency, reduce costs, and enhance product quality. Deevio, an AI-driven startup specializing in machine vision technology that uses deep learning for image classification in the fields of industrial quality control, found automation applications in various industries, from automotive and manufacturing to food processing and logistics. Their approaches showcase some of innovative potential that machine vision offers to those working in industrial automation.

Machine vision systems bring simplicity to complex processes, and as a result, form a key part of industrial automation. These systems use cameras and image processing to inspect, identify, and guide processes, thus enhancing quality control and accuracy. Initially, industrial automation focused only on increased productivity, but thanks to advances in machine vision hardware, now the focus has expanded to quality and flexibility, enabling manufacturers to accomplish a wide range of tasks. However, none of that is possible without good quality data. Being able to accurately capture, analyze, and understand data is fundamental to the success of machine vision for industrial automation.

Applications of Machine Vision

Machine vision systems are a critical component in various aspects of industrial processes owing to their versatility and precision. Their technology enables industrial machinery to see and act upon the information it receives. These systems are purpose-built to operate in real time, seamlessly integrating into production lines to boost efficiency, accuracy, and uniformity.

Machine vision systems excel at inspecting products for defects and ensuring they meet quality standards. By identifying imperfections—such as cracks, discolorations, or incorrect assembly—at high speeds and with remarkable accuracy, these systems are also ideally suited to assess the surface quality of objects, detecting scratches, blemishes, or contamination. They ensure quality control further by classifying products based on visual characteristics and then sorting items into categories for processing or packaging. Machine vision guides robots in picking, placing, and assembling items with precision. This application is especially vital in automotive manufacturing, where robots work alongside humans. Machine vision systems’ ability to track and manage inventory by reading barcodes and quick response codes helps reduce errors, while their ability to verify proper packaging and labeling of products ensures regulation compliance.

Vital Components of Machine Vision Systems

Each element of machine vision systems is vital to delivering high-quality data that lays the foundation of industrial automation. Having the right light and cameras, for example, is essential to capturing highly detailed images that contribute to classification and processing. In fact, machine vision systems consist of several key components that work in tandem to capture, process, and analyze visual data efficiently for industrial automation tasks, including the following:

Cameras: High-quality industrial cameras capture images or video footage of objects or processes being inspected. These cameras come in various types, such as area scan cameras, line scan cameras, and 3D cameras, depending on the specific application requirements.
Optics: Lenses, filters, and lighting equipment are crucial for ensuring proper image acquisition. Optics help control factors like focus, depth of field, and illumination, which are essential for image clarity.
Illumination: Controlled lighting is necessary to consistently illuminate objects being inspected. Proper illumination enhances contrast and reduces shadows so the system can identify features more easily and accurately.
Processing Hardware: High-performance computers or embedded systems process image data and execute machine vision algorithms. Graphics processing units (GPUs) are often used for parallel processing to accelerate computations.
Robotic Integration: In some cases, machine vision systems are integrated with robotic arms or other automation equipment to facilitate actions based on the inspection results.
These components collectively form a robust machine vision system tailored to the specific needs of industrial automation, enabling applications such as quality control, defect detection, sorting, and product traceability.

Benefits of Deep Learning for Machine Vision

Deep learning has revolutionized machine vision by significantly improving its accuracy and capabilities. Traditional machine vision systems relied on handcrafted features and rule-based algorithms, limiting their adaptability to complex visual tasks. In contrast, deep learning algorithms, particularly convolutional neural networks, excel at automatically learning hierarchical features directly from raw data. Deep learning’s data-driven approach and scalability are game-changing for machine vision, enabling applications in diverse fields.

Thanks to deep learning, machine vision systems can handle intricate tasks such as image classification, object detection, segmentation, anomaly detection, and optical character recognition with unprecedented accuracy. Deevio, for instance, uses deep learning frameworks such as TensorFlow and PyTorch, both of which offer hardware accelerators that further enhance deep learning performance. Nonetheless, even old architectures such as ResNet would work with good quality data.

Importance of Data Quality for Machine Vision Systems

Data are fundamental in advanced machine vision systems for industrial automation. Good, clean data can help accomplish almost any task. In contrast, if you don’t have good data, machine vision will not be equipped to optimize industrial automation, no matter how good your deep learning models are. To ensure high-quality data, you need a robust pipeline with high-quality hardware, lenses, cameras, lighting, and more. The next step is understanding what you’re looking for and creating good definitions. Only then can you train a model to excel at a deep learning task.

The next issue impacting data quality is data drift—the gradual and often unnoticed changes in the characteristics of the data used for training deep learning models. It typically involves alterations in sensor readings, environmental conditions, or production processes over time. Data drift poses a significant challenge as models trained on historical data may become less accurate or even obsolete when faced with new, unseen data. Continuously monitoring and adapting deep learning models is essential to address data drift, thus ensuring that predictive and control systems remain reliable and effective as conditions evolve.

Another significant issue is data leakage, which occurs when a model accidentally learns something other than the actual target. This can happen inadvertently if the position of a part shifts slightly during data collection and causes the model to observe and learn something unintended. Such changes in position can be barely perceptible, but since these systems are extremely sensitive, the outcomes can critically hinder machine vision capabilities. This is a huge problem, especially in data automation, because we’re dealing with very small variations; the model must learn the defect, not something in the background. Data leakage may compromise the model’s integrity and lead to overly optimistic performance estimates. Preventing data leakage requires careful data preprocessing, feature engineering, and, ultimately, ensuring that models are trained and tested with the correct information.

Emerging Trends in Machine Vision for Industrial Automation

The biggest future trend in machine vision is the integration of large language models (LLMs), like ChatGPT, to create even more powerful and versatile artificial intelligence (AI) systems capable of comprehensive and context-aware interactions. When this happens, humans won’t need to label data and train models. Instead, they’d be able to ask any question, such as what can be seen on the image, receive a full-level description and ask follow-up questions to get more specific information. While industrial automation is a very specific domain and most open-source AI language models today are far more general, there is always an option to fine-tune a model to specific needs and employ the full possibilities of the current state-of-the-art approaches. Advanced hardware accelerators, such as GPUs and TPUs, will still be needed to train next-level transformers. However, the future of these models is likely to be far more accessible on different devices, including PCs and edge devices. This is due to the development of techniques for quantizing LLMs, which results in more lightweight models that can be run on less powerful hardware.

Conclusion

Machine vision systems are a cornerstone of industrial automation. They use cameras and image processing to inspect, identify, and guide processes, thus enhancing quality control and accuracy. Advances in machine vision hardware make industrial automation more efficient and flexible by enabling many types of tasks to be completed. Yet, data quality remains critical. Regardless of how advanced your deep learning model is, poor data quality will prevent true automation and optimization. Data acquisition includes both the ability to understand what you are looking for and the right technology to accurately capture and analyze that data. Only then will you be able to train models and deliver fast and accurate results. Finally, with the potential of integrating LLMs into machine vision systems, industrial automation is poised to take those results to new heights.