Application of Machine Learning in Image Analysis in Industrial Vision Systems

Introduction

Industrial vision systems are combinations of cameras and software that enable machines to “see” and make decisions based on visual data. They are used for quality control, dimensional measurements, part identification, and guiding robots on production lines. Traditionally, many such systems were based on manually designed algorithms (rules) — e.g., simple programs checking bottle fill levels or part dimensions.

However, with the advancement of artificial intelligence, machine learning methods are playing an increasingly important role. These methods allow the computer to “learn” to recognize patterns from data, instead of relying solely on predefined rules.

In recent years, deep neural networks (deep learning) have revolutionized image analysis — enabling the automation of tasks that traditional approaches could not handle effectively. Below, we explain the key concepts (artificial intelligence, machine learning, neural networks, deep learning) in the context of image analysis and their industrial applications. We also compare the hardware requirements and processing speed of these techniques on local devices (without using cloud computing).

Artificial Intelligence vs. Traditional Vision Systems

Artificial Intelligence (AI) is the broadest concept encompassing all techniques that allow machines to mimic human intelligence in solving complex tasks. Not all AI methods learn from data — classical AI systems were often based on logic defined by experts (so-called expert systems).

In the context of image analysis, this includes traditional rule-based vision systems, where engineers program specific image processing algorithms — such as thresholding, shape matching, edge distance measurements, etc. These types of systems perform well in tasks that can be mathematically defined — such as precise dimensional measurements, detection of high-contrast defects, product identification, or part positioning.

Such systems operate deterministically and are typically very fast — analyzing a single image frame takes only tens of milliseconds on an industrial PC or even on an embedded camera processor. As a result, traditional machine vision systems can inspect hundreds or thousands of objects per minute, with full coverage of the production line.

Another advantage of rule-based systems is transparency — it’s easy to trace why the system marked a particular object as defective (it results from a specific rule). The limitation, however, lies in the lack of flexibility: when object characteristics or lighting conditions change, the engineer often needs to manually adjust algorithm parameters or even develop new rules.

If a defect or characteristic cannot be described using simple metrics (e.g., fine scratches with irregular shapes on a surface), or if the product’s appearance is highly variable (e.g., natural differences between fruits), traditional vision systems reach the limits of their effectiveness. In such cases, learning-based methods are used, which can generalize knowledge based on examples.

Machine Learning (ML) in Image Analysis

Machine Learning (ML) is a subset of artificial intelligence focused on algorithms capable of learning from data, rather than being governed solely by programmed rules. In practice, this means that the software trains a model on examples (e.g., images), adjusting its internal parameters to perform a given task better and better — for example, to distinguish between good and defective products.

Machine learning has been used in vision systems for years, even if it wasn’t always explicitly referred to as “artificial intelligence.” For instance, the popular pattern matching tool learns from a reference image what a correct object looks like, and then finds similar objects in new images — this too is a form of machine learning based on training a feature-based classifier.

How does it work? First, a training dataset is required — in the case of machine vision, these are typically images of objects labeled with annotations (e.g., which images show a good product and which a defective one, or what classes of objects are present). Next, a machine learning algorithm is selected and trained using this data, which usually involves optimizing an error function so that the model correctly predicts image labels.

In traditional ML, data preprocessing is often necessary — instead of feeding raw images, the engineer extracts relevant features (called feature extraction), such as color statistics, texture, or shape outline. These extracted features (a feature vector) are then fed into the learning algorithm. This requires domain knowledge — the expert must identify which features of the image are useful for distinguishing between classes. For example, in fruit classification, a simple ML model might use the object’s roundness and surface area in pixels to distinguish apples from pears.

Other popular ML algorithms used in image analysis include:

• k-NN (k-Nearest Neighbors) – a classification method that assigns class labels based on the majority of labels among the k most similar (in feature space) known examples; it’s simple, fast, and effective for recognizing simple patterns, and is commonly used in OCR.
• Decision Trees and Random Forests – algorithms that create models in the form of a condition tree (or multiple trees), making classification decisions based on image features; they are useful in less complex tasks where clear decision rules can be extracted, or for analyzing hyperspectral data.
• Shallow Neural Networks (e.g., MLP – Multi-Layer Perceptron) – models inspired by neural connections but with only one or a few hidden layers; they can capture non-linear relationships in data but generally fall short of deep neural networks in more complex tasks.

Advantages of traditional ML

Compared to deep learning (discussed later), classical ML algorithms typically require less training data and can be trained faster. Often, a few dozen or a few hundred images are sufficient to train a model to distinguish between 2–3 classes of simple objects effectively. Moreover, these models are usually lightweight computationally — training takes seconds to minutes rather than days. Inference (running the model) is often possible even on low-power processors without hardware accelerators, making deployment on typical industrial systems easier. Scientific literature highlights that traditional ML algorithms can be more efficient than complex neural networks in many machine vision tasks — especially when the problem is well-defined, and data variability is limited. Additionally, ML model results are usually easier to interpret (e.g., you can track which feature influenced the classification), while deep neural networks are often seen as “black boxes.”

Limitations of ML

The main challenge is the need for feature extraction — creating an appropriate set of features can be time-consuming and requires expert knowledge. Even so, the model may be sensitive to changes in conditions (e.g., different lighting alters feature values). ML models typically perform poorly on highly complex or unstructured images, where it’s difficult to manually define distinguishing features. When the number of possible object variations is large (shape, texture, orientation, background, etc.), the performance of shallow models drops dramatically — in such cases, neural networks that can learn complex data representations on their own are a better fit.

Artificial Neural Networks

Neural networks are a family of machine learning algorithms inspired by the structure of the biological brain. A neural network model consists of many simple processing units — neurons — organized in layers that process input signals and pass the output to subsequent layers. Each connection between neurons has an assigned weight that determines the strength of the signal’s influence. During the training process, these weights are adjusted so that the network produces the desired outputs.

Neural networks are capable of approximating complex nonlinear relationships — even a single hidden layer allows the model to learn basic nonlinear patterns, and adding more layers enables the representation of increasingly abstract data features (e.g., early layers may detect edges in an image, while deeper layers identify combinations of edges forming specific shapes, and so on). In the context of image analysis, artificial neural networks have been used since the 20th century — a classic example is handwritten digit recognition. As early as the 1990s, small convolutional networks could recognize postal codes on envelopes with high accuracy, despite having only a fraction of the computing power available to modern models. These shallow networks (e.g., multilayer perceptrons – MLPs) typically consisted of 1–3 hidden layers and a small number of neurons. They were computationally lighter than today’s deep networks, making them feasible to train and deploy on the hardware available at the time. However, they were less effective in complex tasks (e.g., recognizing thousands of object classes). For many simple industrial applications, MLPs or other shallow networks are still useful — for example, in analyses where the signal can be described by a few relevant variables. However, the real breakthrough in machine vision came with the development of deep learning — i.e., neural networks with many layers — which significantly expanded the capabilities of vision systems.

Deep Learning

Deep learning is a subset of machine learning that uses deep (multi-layered) neural networks for automatic data analysis. In simple terms, deep learning involves training artificial neural networks with far more hidden layers than traditional models. While a basic neural network might have one or two hidden layers, deep networks can have dozens or even hundreds of layers, enabling them to learn highly complex data representations.

In the case of images, the most popular architecture is the convolutional neural network (CNN), specifically designed for processing visual data. CNNs automatically extract features from images by scanning them with filters (convolutional kernels). The early layers learn to detect simple patterns (edges, textures), while deeper layers identify more complex shapes or objects. As a result, deep networks learn features without human input, minimizing the need for manual feature definition. This is a key difference: the network learns by itself what to pay attention to, optimizing itself for the task — which makes deep learning exceptionally effective in problems where we don’t know in advance which features are important.

Applications of Deep Learning in Machine Vision

Deep learning has enabled the automation of many tasks that were previously difficult or impossible to perform using rule-based logic or shallow models. In industrial settings, deep learning is used for:

• Anomaly and defect detection — networks learn to recognize whether an image deviates from the expected pattern (e.g., detecting scratches, material flaws, or print errors, even when the defects have irregular shapes).
• Object classification in images – e.g., recognizing the type of product, assessing quality (good/defective), visually sorting goods (such as grading fruits by quality class).
• Object detection and localization – indicating where a specific type of object is located in the image (e.g., counting items on a conveyor, checking for the presence of all components in an assembled electronics module).
• Image segmentation – dividing an image into regions belonging to defined classes (e.g., separating defects from the background surface, isolating regions of interest for further analysis).
• Text reading (OCR) and character recognition – modern Deep OCR algorithms can read even distorted, variable, or low-contrast text on products where traditional OCR fails.

Importantly, deep learning often outperforms humans in repetitive visual inspection tasks — networks can detect subtle differences invisible to the naked eye, and do so with high consistency and no fatigue. For example, for a human inspector, evaluating each individual fruit for all potential defects is tedious and subjective; a neural network can learn acceptable ranges of natural variation (spots, skin discoloration, etc.) and apply quality criteria uniformly.

Deep Learning Requirements and Challenges

However, the effectiveness of deep learning comes at a cost. Deep learning models generally require large training datasets and substantial computational power for training. Training a deep network is an iterative optimization process of thousands or even millions of parameters — typically requiring hundreds or thousands of images and hours of computation on powerful graphics processors (GPUs) or tensor processing units (TPUs). This training process can be time-consuming — often lasting many hours or days, and in extreme cases, weeks.

The implementation of deep learning in vision systems can be simplified by using industrial software packages that offer pre-parameterized network models for specific tasks (e.g., anomaly detection, object localization, object classification) and tools for preparing training datasets (including image augmentation to improve abstraction in small datasets). One example is Zebra Aurora Vision Deep Learning™, which significantly reduces training time, lowers the required dataset size, provides network compression, and includes an efficient inference engine. The software also includes a pre-trained, ready-to-use model for Optical Character Recognition (OCR), eliminating the need for custom training.

Another challenge is explainability — as mentioned earlier, deep learning is often a “black box.” It is difficult to interpret why the network made a specific decision (e.g., flagged a defect). In industrial applications, where reliability and auditability are critical, this may raise concerns (though there are XAI – explainable AI – techniques aimed at providing insight into neural network behavior). Despite these limitations, the benefits of deep learning — especially its leap in accuracy and capability — make it a foundational technology in modern industrial vision systems.

Required Computing Power and Local Processing Speed (Without Cloud Use)

Local (edge) processing plays a key role in industrial vision systems, as they require real-time operation and reliable performance independent of an internet connection. In applications such as production line inspection, every millisecond matters — decisions (e.g., rejecting a defective product) must be made instantly. That’s why all computations are performed directly on the device instead of sending images to a cloud server. Eliminating network delays ensures a deterministic and low response time, which is critical for synchronization with fast-moving conveyors or robots. Another benefit of local execution is data security — images (which often contain company intellectual property, such as new products) never leave the factory, making it easier to comply with data protection and privacy regulations.

Classical vision systems and simple ML models typically run on standard hardware: an industrial PC or even an embedded processor in a smart camera is often enough to meet real-time demands. As mentioned earlier, rule-based algorithms and simpler learning models require significantly less computing power than deep networks — their CPU/GPU demand is minimal, and training time is short. Many ML libraries (e.g., Zebra Aurora Vision, OpenCV) run efficiently on CPUs without requiring specialized hardware or cloud services. As a result, ML-based inspection (e.g., SVM classifying defects based on a few features) can often be implemented without additional infrastructure costs — such models can run on typical industrial PCs, maintaining real-time image processing (even within tens of milliseconds per frame).

Deep learning, however, imposes higher hardware demands. During training, accelerated hardware is required — GPUs are the standard since they can process thousands of parallel operations needed for network learning. Without GPU acceleration, training large datasets would be prohibitively time-consuming. Moreover, even inference (real-time prediction) with deep networks can be resource-intensive — especially when the model is complex and needs to process many frames per second. While a single prediction can often be performed in a fraction of a second on a CPU, reaching 60 FPS with a large CNN may require GPU acceleration. In practice, many solutions use local GPUs or specialized smart cameras with built-in accelerators that can run vision networks directly on the camera image stream.

To summarize: Machine Learning and Deep Learning can be successfully implemented on local devices, but the hardware platform must be matched to the application’s needs. For simple classification tasks, a CPU may suffice; for more complex tasks, a GPU or a specialized chip (like an NPU) may be needed. Model optimization is also important: in edge applications, techniques like quantization or pruning (reducing the size of the network) are commonly used to decrease computing load with minimal accuracy loss. The goal is to achieve stable response times that match production speeds.

Conclusion

Artificial intelligence solutions are increasingly entering industrial vision systems, boosting both performance and flexibility. AI is a broad concept that includes both traditional rule-based algorithms and modern learning-based approaches. Within AI, we distinguish Machine Learning (ML) — methods where models learn from data. Classical ML algorithms like decision trees, SVMs, or shallow neural networks have long improved machine vision tasks, especially when problems are well-defined and features can be extracted manually. Neural networks are the foundation of most modern achievements in AI – they mimic the biological brain and are capable of learning complex nonlinear relationships.

When a neural network becomes very deep, we refer to it as deep learning – an approach that currently dominates advanced image analysis. Deep learning has enabled solutions to problems that were previously unsolvable: from reliably detecting minute surface defects, through recognizing objects in complex scenes, to interpreting medical images or controlling autonomous vehicles. The choice of method depends on the specific task: simplicity vs. complexity, interpretability vs. accuracy, data requirements vs. computational power – all these factors must be considered. Often, the best results come from combining approaches – for example, a classical vision algorithm precisely locates the region of interest, and a deep learning network evaluates whether a defect is present. Regardless of the method, the key point is that modern vision systems can operate in real time on local devices, thanks to advances in both hardware (fast processors, AI accelerators) and algorithmic improvements. As a result, industry has gained powerful tools for automating quality control and production processes, increasing efficiency and reducing costs while maintaining high product quality.