Computer vision in manufacturing:
9 use cases, examples, and best practices

Computer vision in manufacturing: 9 use cases, examples, and best practices

May 23, 2023

Computer vision in manufacturing: a market overview

Computer vision technology and the manufacturing sector look set to develop closer ties in the years to come.

of manufacturers consider computer vision important for meeting their business goals

IBM, 2021

of the global computer vision market is covered by its industrial segment alone

Grand View Research, 2021

CV market CAGR 2023-2030, with manufacturing as one of its fastest-growing segments

Mordor Intelligence, 2023


Advanced data analytics including predictive/prescriptive analytics


IIoT/IoT data from devices

Artificial intelligence (AI) and machine learning (ML)

Computer vision

Autonomous systems

Augmented/virtual/mixed reality

Next generation ERP systems

Track and trace for supply chain visibility

Scheme title: Top 10 technologies helping manufacturers meet their business goals
Data source: — 2021 Digital transformation assessment Covid-19: a catalyst for change

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An example of a manufacturing-oriented computer vision system

A study by the Institute of Electrical and Electronics Engineers suggested a potential system framework to integrate computer vision into your existing manufacturing environment. These are its key components and related processes:

Scheme title: Main components of a computer vision system for manufacturing
Data source: IEEE — Computer vision techniques in manufacturing, 2022

LightingmoduleLightManufacturingsystemOptical imageSensing module(cameras)ImageInteractionActuator(robots)DecisionDecision-making(strategies)Feature descriptionsCV system(algorithms)Optical devicesHardwareSoftware

Lighting module

Provides the sensing module with uniform illumination and enables it to capture clear images of the manufacturing system.

Manufacturing system

Encompasses assembly lines, assembly robots, and automated guided vehicles.

Sensing module

Comprises cameras that collect visual data from the manufacturing system and send it to the CV system via the Internet of Things (IoT).

Computer vision system

Typically powered by machine learning (ML) algorithms, it can perform different tasks from product counting to execution quality control with defect (anomaly) detection.

Decision-making module

Processes the previous results with rule-based or more complex artificial intelligence algorithms to trigger a suitable course of action.


A set of robots that interact with the manufacturing system according to the strategies defined by the decision-making module.

9 use cases of computer vision in manufacturing

Computer vision systems can be deployed in many use cases and operational scenarios within manufacturing processes.

1 Quality inspection

Combined with ML-powered anomaly detection, computer vision enables automated visual inspection to identify defective products with any type of anomaly (e.g. scratches or dents).

2 Assembly process automation

Computer vision technology can automate the entire assembly process. It can detect parts, orient them correctly to the assembly line, and then track their progress as they move through the various stages of the production process.

3 Packaging inspection

Manufacturing companies rely on computer vision solutions to ensure products are correctly packaged, palleted, and labeled with appropriate barcodes.

4 Object detection and recognition

CV can identify and track objects within manufacturing operations, such as parts on a conveyor belt or products on an assembly line, allowing for real-time supervision.

5 Compliance monitoring

Computer vision systems can continuously monitor adherence to relevant manufacturing compliance rules, safety regulations, or environmental standards.

6 Sorting and counting

Manufacturers use computer vision to sort and count items, such as parts or components. Automatic sorting helps to improve accuracy, particularly in high-volume production environments.

7 Dimensional measurement

Computer vision helps manufacturers automatically measure the dimensions of products or components, ensuring they meet specific size or shape requirements.

8 3D vision and design

Product design software can implement computer vision features to scan existing components, create their 3D models, and facilitate the prototyping of new items.

9 Supply chain traceability

Along with RFID technology, computer vision can track the movement of products across the supply chain to ensure better visibility, optimize routes and execute inventory management, identifying shortages or excesses.

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Examples of computer vision in manufacturing

Here are a few real-life examples of manufacturing companies leveraging computer vision in a variety of processes:

Dow Chemical

Dow, the third largest chemical producer in the world, implemented an Azure-based CV solution to enhance employee safety and security. The system performs several tasks, including personal protective equipment monitoring and containment leak detection.


Volvo’s computer vision system Atlas can scan each vehicle with over 20 cameras to spot surface defects, detecting 40% more deviations than manual inspections. The entire cycle takes between 5 and 20 seconds, depending on the size of the vehicle.

Image title: Volvo Cars’ visual inspection system Atlas
Data source:—AI-based vision technology aids vehicle inspection


Komatsu Ltd, the world’s second-largest construction equipment manufacturer, partnered with NVIDIA to adopt a safety-focused computer vision solution. The platform can monitor the movement of workers and equipment to signal potential collisions or other dangers.


Different kinds of robotic assistants are common in the manufacturing industry. However, computer vision-enabled robots can perform more complicated operations, requiring decision-making. For example, a Tennessee-based plastic injection molding corporation Tennplasco deployed Sawyer Robot, a multipurpose robotic arm equipped with a camera, to recognize and pick up objects even when they’re unsorted. As a result, the company achieved the expected ROI in less than four months.

Benefits of computer vision in manufacturing

The adoption of computer vision systems in industrial processes can strengthen the manufacturing sector in terms of:

Greater productivity

According to Deloitte, adopting computer vision, automation, and other smart factory initiatives accelerates manufacturing cycles, resulting in a 12% growth in labor productivity and 10% in total production output.

Cost optimization

Increased productivity and minimized machine downtime via automation and computer vision-based maintenance (up to 50%, based on McKinsey's estimates) translate into a general reduction in operating costs.

Improved quality

Computer vision-driven robots operate with surgical precision, ensuring better product quality, minimized human error, and an overall reduction of 10-20% in QA operations cost, as reported by McKinsey.

Workforce safety

Deloitte highlighted that computer vision systems in manufacturing enable organizations to identify dangerous malfunctions, monitor workers' conditions, and detect any sign of fatigue or discomfort.

Computer vision adoption roadmap for manufacturers

Implementing an ML-based computer vision solution in a manufacturing scenario will involve business-oriented and technical steps. Here’s a brief rundown of the main adoption stages.


Business needs analysis

Analyze customer business needs and expectations 

Assess the current tech ecosystem

Frame corporate needs and goals

Define the solution’s scope and requirements


Initial data analysis

Map and assess corporate data sources

Identify external data sources (public databases, etc.)


Solution design

Design the solution’s architecture

Set up a project plan, including budget and timeline

Identify a suitable tech stack

Define the scope of a PoC, if required


Building the solution

Perform data cleansing, annotation, and transformation

Define the solution’s assessment criteria

Process data with algorithms

Build a CV model trained to recognize patterns


Integration and rollout

Integrate the CV model into the solution

Deploy the product in the target environment



Fine-tune the CV model with new data

Perform ongoing maintenance, updates, and fixes

Computer vision adoption challenges and best practices

When implementing computer vision in manufacturing, you can face some technical challenges. Keep in mind the following recommendations to streamline its adoption.

IoT networks of computer vision-based devices may encompass various types of hardware that rely on multiple communications protocols and handle different data formats (from simple images to infrared thermography).
Communication between such devices and your CV management and analytics software will require suitable APIs. Cloud tools such as Google Cloud Data Fusion or Amazon API Gateway provide pre-built APIs and facilitate their development. However, the integration sometimes requires a middleware architecture like an ESB to convert different protocols.
Analyzing large volumes of unstructured data in real time requires remarkable processing capabilities. Furthermore, the ML algorithms typically involved in image processing should be trained on large data sets.
Сloud providers offer comprehensive ML services for visual data analysis. For example, Amazon SageMaker, Amazon Lookout for Vision, Azure Cognitive Service for Vision, and Google’s Vision AI provide scalable compute resources, pre-trained ML models, and built-in algorithms. Also, consider relying on edge computing to distribute processing workloads and minimize latency.
Interconnected ecosystems of computer vision-powered devices and data analytics software suffer from multiple vulnerability points, which can trigger cyberattacks and provoke breaches or data leaks.
You can protect your hardware and software with different tools and features. These include identity and access management based on a zero-trust approach, event management, encrypted data exchange via cryptographic protocols (including Transport Layer Security), and IoT device authentication options (such as X.509 certificates).
A journey towards Industry 4.0

A journey towards Industry 4.0

Along with other technologies driving digital transformation in manufacturing and the shift towards the Industry 4.0 model, computer vision proved to be a valuable ally for this sector. Indeed, its implementation has often resulted in significant cost cuts, superior quality control, higher production output, and increased worker safety.

To reap the full benefits of this technology, however, companies will need an upgraded tech ecosystem, a solid data management and integration strategy, and the expert guidance of a trusted partner like Itransition.

A journey towards Industry 4.0

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How is computer vision used in manufacturing?

Computer vision systems cover various use cases and operational scenarios. These include, for example, product design, automated product assembly, quality inspection, predictive maintenance, inventory management, supply chain traceability, and safety monitoring.

How reliable are computer vision systems in manufacturing?

Thanks to modern deep learning algorithms and neural networks, computer vision systems for object or anomaly detection and other manufacturing-related tasks have achieved close to 99% accuracy, as reported by SAS.

What is the difference between computer vision and machine vision?

While they partially overlap, computer and machine vision diverge in terms of focus.

  • Computer vision
    represents a broader field that includes tools and techniques to capture, process, and understand visual inputs for both analytical and practical purposes. However, the emphasis is on advanced cognitive skills, data analytics, and decision-making.
  • Machine vision
    is a more specific term referring to the use of vision systems for industrial or manufacturing applications, including inspection, quality control, and automation. Machine vision uses specialized hardware and software to capture and analyze visual data in real time, such as measuring dimensions, detecting defects, and identifying patterns.

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