How to Deploy an AI-Based Machine Vision Application
AI makes it possible to detect subtle defects, adapt to part variability, and anticipate production drifts. Written by Y.Belgnaou.
On production lines, quality control by camera can now rely on image processing tools powered by artificial intelligence techniques. While traditional vision systems implemented tools based on fixed rules, AI now makes it possible to detect subtle defects, adapt to part variability, and anticipate production drifts. Here are some key principles to follow when deploying an AI-based machine vision application. It all starts with a clear definition of the problem to be solved. Is the goal to identify microcracks invisible to the naked eye, check assembly positioning, or sort products based on their appearance? This first step determines all the ones that follow: how data is collected, the choice of camera, hardware architecture, and even the type of algorithm selected. A poorly defined objective inevitably leads to an ineffective training process — like teaching an operator to recognize a defect without ever explaining what makes it a defect.
Image collection is at the heart of the project. Contrary to common belief, it's not enough to accumulate a large number of photos: they must accurately represent the real conditions of inspection. This often means capturing images using the same camera and lighting setup that will be used in production. The images should reflect the full variety of possible cases: conforming parts, defective parts, batch variations, and lighting differences. This is called “locking” the scene — stabilizing physical parameters so the AI doesn’t have to compensate for unnecessary variations.
Neural Network Training
Once the data is collected, the next step is to train the neural network. Several options are available. Companies can leverage the processing power of dedicated GPUs or specialized integrated circuits (ASICs) to accelerate performance, or outsource this stage to the cloud to avoid investing in expensive hardware. Some integrated solutions even allow the model to be trained directly within the camera, without an intermediate PC, reducing data transfers and simplifying deployment.
This wide array of tools reflects the evolution of the market: alongside deep learning software platforms designed for engineers, there are now smart cameras capable of combining traditional vision tools with AI, using “point-and-click” graphical interfaces accessible to users with no programming skills. These systems lower the entry barrier, but they do not eliminate the need for domain expertise. It’s still the knowledge of the industrial process, the parts, and the types of defects that guides dataset construction and results validation.
Image Processing
Integration into the production line typically follows two approaches: processing the image as close to the source as possible, directly inside the camera (“Vision at the Edge”), or sending it to an industrial PC or cloud server. The first reduces latency and infrastructure requirements; the second offers greater flexibility for more complex analysis. In both cases, the system must communicate with existing PLCs and automation systems through standardized interfaces, so inspection results can trigger appropriate actions — line stoppage, product sorting, or operator alerts.
The final step is validation. Before full deployment, models must be tested on real parts and under varied scenarios. Performance matrices, which show how the AI classifies good and defective parts, help evaluate reliability and identify weak spots. This phase, often carried out as a pilot project, provides an opportunity to refine the model, expand the image database, and confirm that the system meets the original requirements.
A Well-Structured Process
Deploying an AI-powered machine vision application is neither a simple matter of assembling hardware nor a domain reserved for data scientists. It’s a structured process in which each step — defining the objective, collecting data, selecting tools, training the model, integrating the system, and validating the outcome — plays a critical role in overall success. While technology has evolved to make some tasks more accessible to non-specialists, a rigorous approach and clearly defined goals remain the real drivers of performance.
