When implementing computer vision, manufacturers often face a choice: should they go for a ready-made, off-the-shelf tool or build a custom computer vision solution tailored to their unique needs? The answer is: it depends. In this article, we discuss the technology that is available off the shelf versus custom computer vision development, and share best practices to ensure reliable system performance. Let’s start.

1. What is a Computer Vision Solution?

Computer vision solution (CV for short) is a technology that uses cameras or sensors to acquire visual data and software that helps analyze what is that data about. It mimics human vision. Cameras like human eyes collect information, while software analyzes what’s being perceived. This technology is used by manufacturing giants like Coca-Cola (bottle inspection), Siemens or Nestlé. It helps manufacturers automate tasks that would typically require human inspection. Those tasks include:

• Defect detection – computer vision can spot inconsistencies and flaws that appear on the surface of the product or internally.
• Foreign object detection – such technology can also inspect the production line or manufacturing environment to find contaminants or objects that can influence the quality of end-product.
• Label scanning – it helps technology that scans labels, barcodes, and QR codes to retrieve information.
• Product sorting – CV can be trained to classify products and help create automatic sorting systems.
• Product assembly – computer vision can “give vision” to robots and help them assemble products.
• Quality inspection – manufacturers can train vision-based solutions to spot lower quality products.
• Safety inspection – smart vision systems can check if workers wear safety gear and help factories comply to safety standards.

Read more about the practical applications of Computer Vision here.

2. What Are the Most Popular Computer Vision Systems?

There are many computer vision systems that manufacturers can buy online and install in their factories. They are often referred to as machine vision or industrial vision systems, because they’re integrated into the machines or just to emphasize the industrial application of computer vision. The most popular vision systems that are available off the shelf are the tools below.

Cognex Machine Vision Systems

Cognex systems are one of the best machine vision systems out there for surface-level defect detection, part identification and measurement. Their built-in lighting, autofocus lenses, and support for high-speed production make them a top choice for quality control in manufacturing.

Keyence Vision Systems

Keyence offers intuitive, high-speed vision systems for defect detection, dimensional measurement, and robotic guidance. They are praised for easy-to-program interface and automation efficiency. They’re also one of the best machine vision systems for standard production lines

Zebra Machine Vision Solutions

Zebra systems seamlessly integrate barcode scanning with defect detection, offering real-time data analytics to optimize production processes. Their modular design and versatility make them perfect for logistics-heavy industries and fast-moving production lines.

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3. What Are the Benefits of Using an Off-the-Shelf Solution?

Pre-configured CV systems offer numerous advantages, and they are great for manufacturers whose factories follow an uncomplicated production process or produce standard products (such as glass bottles or jars). They offer following benefits:

• Hardware + Software: These solutions often include pre-configured hardware and software bundles that are optimized to work together.
• Quick setup: Off-the-shelf solutions are designed for rapid deployment, allowing manufacturers to integrate them into their industrial lines quickly. This minimizes setup time and reduces the need for extensive customization, enabling immediate improvements in operational efficiency.
• Complete technical documentation: These solutions come with detailed technical documentation, including user guides, installation instructions, and troubleshooting steps. This documentation helps ensure smooth installation, operation, and maintenance, making it easier for manufacturers and technicians to familiarize themselves with the system.

4. What an Off-the-Shelf Solution Might Require?

Lighting Issues

Inconsistent or inadequate lighting can cause significant variability in defect detection. Even with flash or triggered lights, ambient light sources—such as sunlight, ceiling lights, or aging LEDs—can interfere with accurate results.

To improve lighting conditions:

• Shield vision areas from ambient light by installing shrouds or covers.
• Use consistent, directional, and diffuse lighting.
• Replace old lights regularly—LEDs degrade over time and may lose intensity.
• Block or filter external light using IR filters, blackout curtains, or anti-glare windows.
• Adjust the angle and type of light to match your needs (e.g., ring vs. bar light, IR vs. visible spectrum).

Environmental Changes

Physical and environmental factors like vibrations, dust, debris, or even unexpected objects (such as granola bar wrappers!) can disrupt vision accuracy.

Solutions:

• Clean the camera lens and its surroundings regularly.
• Maintain a clutter-free area around the vision system to avoid unpredictable variables.
• Track maintenance logs to identify and correlate inspection failures with changes in the environment.

Camera Setup

Misalignment, focal drift, or using cameras with inadequate resolution can result in inconsistent image captures.

Solutions

• Verify that the camera resolution and focus are sufficient for detecting your smallest defects.
• Re-calibrate the system regularly, especially after any mechanical changes or adjustments.
• Ensure all cameras and mounts are securely fastened and resistant to vibration.

Software Limitations

Software algorithms may behave differently in a live production environment compared to offline testing, especially if there are discrepancies in software versions or configurations.

Solutions:

• Compare online (real-time) and offline detection logic and confirm software versions match.
• Improve the algorithm with more robust features, thresholds, or decision logic.
• Use a labeled validation dataset of good and bad parts to fine-tune detection parameters.

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Other Best Practices to Follow

• Save Image Analysis: Always save captured images along with timestamps and associate them with the corresponding inspection results. This practice helps in debugging inspection issues by allowing you to determine whether a part was misclassified due to a bad image, algorithmic error, or random interference (like an object obstructing the view).
• Upgrade to Smart Vision: Consider upgrading to intelligent vision systems such as Keyence IV2, which use convolutional neural networks (CNNs). These are generally more tolerant of lighting variability and require less manual tuning, offering higher accuracy and reliability in complex environments.

Here’s a table with common drawbacks of a ready computer vision tool in terms of 6 key areas and how to deal with them.

Problem Area	Key Action
Lighting	Ensure uniform, shadow-free lighting. Isolate from external light. Replace aging LEDs.
Environment	Eliminate ambient light interference. Keep lenses and surroundings clean.
Camera Calibration	Regularly recalibrate. Check mounting stability and confirm proper focus/resolution.
Algorithm Accuracy	Use a large labeled dataset for testing. Adjust feature logic or thresholds as needed.
Debugging Tools	Implement timestamped image logging for offline review and issue tracking.
Hardware Upgrades	Consider smarter systems (e.g., ML-enabled like Keyence IV2) for better adaptability.

5. Who Should Invest in a Custom Computer Vision Tool?

Investing in a custom computer vision system makes sense for manufacturers with unique or highly specialized requirements that off-the-shelf solutions cannot fully meet or they operate in a heavy industry that out-of-the-box CV systems are not prepared for.

• Highly customized products – For manufacturers producing bespoke or one-of-a-kind items, standard CV solutions may not be equipped to handle the complexity of unique shapes, materials, or finishing requirements. For example, a company that manufactures automotive parts with specific surface textures or complex geometries may require custom CV tools that can adapt to these specifications. A pre-built system might not offer the flexibility needed to inspect these parts accurately.
• Unique operational challenges – Low-light conditions, high temperatures, dust, and debris can interfere with detection accuracy of the standard computer vision that manufacturers can buy online. A custom CV system can be tailored to account for these factors, ensuring reliability and precision in even the most demanding settings.
• Advanced defect detection – For manufacturers seeking to implement cutting-edge defect detection techniques, such as machine learning or artificial intelligence, a ready-to-use CV solution may not offer the necessary customization. Custom tools can be specifically designed to include these advanced capabilities, improving accuracy and responsiveness. For example, a semiconductor manufacturer may need a custom CV tool that leverages AI to detect minute defects in electronic components that could otherwise go unnoticed with a standard solution.
• Specialized production processes – Factories with specialized production processes, such as additive manufacturing (3D printing), may encounter challenges with standard CV systems optimized for traditional manufacturing methods. A custom CV tool can be designed to manage the specific inspection requirements of 3D-printed components, ensuring that defects like layer misalignments or material inconsistencies are detected effectively. For instance, a company producing medical implants via 3D printing may need custom software to identify defects affecting the product’s safety and efficacy.
• Integration with existing systems – Ready-made CV tools may struggle to integrate seamlessly with manufacturers with complex, automated production lines or multiple existing hardware systems. A custom solution can be tailored to fit these existing setups, ensuring smooth communication and data flow. For instance, a factory with proprietary conveyor systems, robotics, and automated quality control equipment may require a custom vision tool that integrates tightly with these systems to maintain efficiency and accuracy in defect detection and sorting.

6. Off-the-Shelf vs. Custom Computer Vision: Which Is Better?

If your production process is standard and your defect types are well-defined, an off-the-shelf system is a great starting point. But if you’re aiming for high precision, operate in difficult conditions, or need seamless integration into a complex environment, then a custom solution offers flexibility and performance.

Feature	Off-the-Shelf Solutions	Custom Vision Systems
Setup Time	Fast	Longer (weeks to months)
Cost	Lower initial investment	Higher upfront cost
Flexibility	Limited customization	Highly adaptable
Accuracy in Complex Tasks	Moderate	High
Integration with Existing Tech	May require workarounds	Fully tailored
Long-Term ROI	Lower for complex environments	Higher for specialized lines

7. Examples of Custom Computer Vision Solutions

We chose three examples to illustrate how manufacturers have successfully implemented custom CV technologies to improve quality control, increase efficiency, and reduce operational costs.

Detecting Defects in Wooden Furniture Production

Prohan, a wood furniture manufacturer, faced significant challenges with detecting defects such as gaps, cracks, and splits in glued wooden planks during their production process.

We developed a custom system that could handle the unique characteristics of the production line, including dusty conditions and the need for high-speed inspection. The tailored solution uses machine learning algorithms to automatically identify defects and prevent costly waste, improving overall quality control.

Read the case study.

Computer Vision for Hyper-Precise Manicure Robot

Our partner, 10beauty, sought to develop an automated manicure robot capable of performing five precise steps, including nail shaping and polish application. The critical challenge was creating a computer vision system that could detect nails with 0.25mm accuracy across diverse hand shapes and sizes.

Our CV experts designed a system that enabled precise 3D hand detection, robust nail identification, and automatic regression testing for accuracy, positioning the robot as a game-changer in the beauty industry.

Read the case study.

Automated Validation of Fibre-Optic Installations Using Computer Vision

A leading European fibre-optic provider needed to verify installation quality across multiple stages without relying on time-consuming manual checks. The manual verification process was inefficient and prone to errors.

We designed a custom AI-based solution to automate this process, using deep learning models trained on thousands of installation images. This allowed for automatic checks of both initial and final installation stages, significantly improving accuracy and reducing rework.

Read the case study.

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8. What Can You Expect When Developing a Custom CV System?

Developing a custom computer vision system for your manufacturing environment involves a structured and iterative approach. This process ensures that the solution meets your unique needs, integrates smoothly into your production line, and delivers tangible benefits. Here’s a detailed step-by-step guide to how we do it at DAC.digital.

Step 1
Map out Optimization Opportunities

Once a manufacturer reaches out to us, we aim to identify areas of the production process where computer vision can add the most value. This step sets the foundation for the entire project by clarifying objectives, expected outcomes, and ROI.

We visit production sites to conduct on-site assessments and observe the production environment firsthand. This includes measuring factors like lighting, dust levels, and machine speeds to understand the conditions under which the CV system will operate.

Once we do that, we engage your team in workshops to discuss current challenges and desired improvements. This collaborative environment help us identify key pain points and operational inefficiencies.

Based on the insights gathered, decide whether to use existing technologies or develop a custom solution. The goal is to choose the technology that best fits your requirements, whether it’s a pre-built camera system, a software framework, or a combination of both.

Finally, we determine which processes will most benefit from computer vision, ranging from defect detection and quality assurance to process optimization and real-time feedback.

Step 2
Develop & Implement the Solution

As the next step, we develop a functional prototype and implement the solution into the production line. This step involves creating a PoC (short for Proof of Concept) to validate the core functionalities and building a minimum viable product for quick testing and iteration.

We work with your team to gather and annotate data. This step is crucial for training the machine learning algorithms to recognize defects and anomalies. Depending on the complexity, it might involve manually labeling images as “definitely no defect”, “definitely defect”, and “borderline”.

We configure the necessary hardware and software. This could include cameras, lighting, processing units, and communication protocols optimized for the manufacturing environment. Integration with existing systems (like IoT networks) is key to a smooth implementation.

Next, we test the system in a real-world scenario to validate its performance. Based on these tests, we make adjustments and improvements to ensure the system can handle the unique production environment, including dust, varying lighting conditions, and high-speed production lines.

We fine-tune the system to match the specific requirements of your production line. This includes calibrating cameras, adjusting detection thresholds, and configuring software settings to minimize false positives and maximize detection accuracy.

Step 3
Measure & Optimize the CV System

The last thing to do is ensure that the system maintains high performance over time and can be expanded to meet evolving needs.

Establishing key performance indicators (KPIs) to track the effectiveness of the CV system at this stage is a good idea. These include defect detection rates, waste reduction percentages, or cycle time improvements. Continuous monitoring helps identify areas for further optimization.

Implement machine learning operations (MLOps) to support ongoing system optimization and adaptation. This includes updating algorithms, retraining models with new data, and performing routine maintenance to keep the system running smoothly.

We provide ongoing support to ensure your team can handle the system’s maintenance and future upgrades. This includes training on how to use the system effectively, troubleshoot issues, and make adjustments as needed.

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9. Which is Better – Ready-to-Buy Computer Vision or Custom Development?

Manufacturers should consider their specific needs, production complexity, and desired outcomes when deciding between off-the-shelf industrial computer vision tools and custom computer vision development.

Off-the-shelf computer vision tools are best for manufacturers with standard production processes or less specialized requirements. Proprietary systems might be better for unique challenges, advanced capabilities, and seamless integration. Each approach to integrating computer vision into the factory’s workflow offers distinct benefits, and the decision should align with the manufacturer’s goals, production processes, and ROI expectations.

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