"If you are stuck with old methods and unable to digitize your production processes, your costs may rise, your products will reach the market later, and your ability to deliver unique added value to customers will decline", said Stephen Ezell, an expert in global innovation policy at the Information Technology and Innovation Foundation (ITIF), in an Intel report on the future of AI in manufacturing.
In other words, companies that can quickly transform their factories into intelligent automation hubs will be the ones to gain long-term profits from those investments. These technologies have become essential to both manufacturing and business operations.
According to a recent research report from Forbes Insights, 93% of survey respondents in the automotive and manufacturing sectors classified AI as “extremely important” or “absolutely essential for success.” However, only 56% of these respondents plan to increase their AI spending—and by just 10% or less of their current budgets. This hesitation to invest may stem from a lack of understanding about ROI and the practical applications of AI within their industries.
In visual inspection applications, AI-based image analysis specifically through Deep Learning and Machine Vision can significantly enhance factory efficiency. The combination of rule-based machine vision and deep learning–based image analysis enables robotic assembly systems to precisely identify components, detect missing or misaligned parts, and quickly determine whether an issue exists.
The synergy between machine vision and deep learning forms the foundation for manufacturers to implement smarter technologies, empowering them with greater scalability, accuracy, efficiency, and financial growth for the next generation. However, understanding the nuances between traditional machine vision and deep learning, and how they complement rather than replace each other, is essential to maximize the value of these investments.
Deep learning is a subset of artificial intelligence (AI) and a part of the broader field of Machine Learning. Instead of humans programming computer applications for specific tasks, deep learning uses data and trains it through neural networks to produce more accurate results based on that training data. Simply put, deep learning allows specific tasks to be solved without the need for explicit programming.
Deep learning can consistently recognize anomalies and variations within a dataset on a large scale. This is something humans naturally do well — spotting differences — but something that traditional, rule-based computer systems have not been good at until now. However, unlike human inspectors, computers do not tire when making decisions on an assembly line.
Applications of Deep Learning in Everyday Life: Facial recognition to unlock phones or identify friends in social media photos, recommendation engines on video and music streaming services or e-commerce websites, medical diagnosis, email spam filtering, and credit card fraud detection.
According to O’Reilly Media, there are five major categories of machine learning algorithms:
Supervised learning involves mapping input data to known labels provided by humans. Recommendation tools used by online music and movie services apply supervised learning techniques.
Unsupervised learning uses input data that isn’t labeled, and the system tries to learn the structure of that data automatically without any human guidance. Anomaly detection — such as flagging unusual credit card transactions to prevent fraud — is an example of unsupervised learning.
Semi-supervised learning is often a combination of the first two approaches. The system is trained on a partially labeled dataset — typically, a large amount of unlabeled data and a smaller portion of labeled data. Facial recognition in Facebook and Google’s photo services are practical applications of this approach.
Reinforcement learning is primarily a research field, but industrial use cases are beginning to emerge. It occurs when a computer system receives data within a specific environment and learns how to maximize outcomes based on that data. Google’s DeepMind AlphaGo, which successfully learned how to play the board game Go, is a recent example of this technique.
Transfer learning involves reusing a pre-trained model that has already solved one problem and applying it to a different but related problem. For instance, a deep learning model trained on millions of cat images can later be “fine-tuned” to detect malignant tumors in medical imaging.
At its core, machine vision systems rely on digital sensors housed inside industrial cameras, equipped with specialized optics to capture images. These images are then sent to a computer (PC), where dedicated software processes, analyzes, and measures various characteristics to make decisions.
Traditional machine vision systems perform reliably with consistent, well-manufactured parts. They operate through step-by-step filtering and rule-based algorithms, which are more cost-effective than large-scale human inspection. These systems can operate at extremely high speeds and with great precision — on production lines, rule-based machine vision systems can inspect hundreds or even thousands of parts per minute. The output of this visual data is based on a programmed, rule-based approach designed to solve inspection problems.
In a factory setting, traditional rule-based machine vision is ideal for:
Guidance: Locating, orienting, and identifying key features of a part to optimize other vision inspection tools in later stages.
Identification: Reading barcodes, data matrix codes, direct part markings, and printed characters on parts, labels, and packaging.
Measurement: Calculating distances between two or more points or geometric locations on an object and verifying whether these measurements meet specifications.
Inspection: Detecting defects or other irregularities such as missing safety seals, damaged parts, or other anomalies.
Deep Learning and Human Inspection
More Consistent: Reduces inconsistency between different human inspectors.
More Reliable: Performs dependably even when scaled up or replicated across different production lines.
Faster: Detects defects within milliseconds, enabling high-speed applications and improving throughput.
Deep Learning and Traditional Machine Vision
Designed for Complex Applications: Solves challenging inspection and classification tasks that traditional rule-based algorithms cannot easily handle.
Easier to Configure: Applications can be set up quickly, accelerating feasibility testing and project deployment.
Accepts Variations: Handles defect variations for applications that require tolerance for deviations from strict control.
High-Level Differences Between Traditional Machine Vision and Deep Learning
Development Process: Rule-based programming for each tool vs. example-based training.
Hardware Investment: Deep learning requires greater processing power and storage capacity.
Factory Automation Use Cases: Each approach is suited to different types of automation scenarios.
Rule-based machine vision and deep learning-based image analysis complement each other rather than serving as an either-or choice when implementing next-generation factory automation tools. In some applications, such as measurement, rule-based machine vision will continue to be the preferred and cost-effective option.
For more complex inspections involving wide variations and unpredictable defects — too numerous and complicated to program and maintain in traditional machine vision systems — deep learning–based tools provide an optimal and efficient alternative solution.
Reference: Cognex
>>> Read more: What Is a Vision System?
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