Computer vision in manufacturing: 9 use cases, examples, and best practices
May 23, 2023
Computer vision (CV) in manufacturing involves AI-powered systems that can capture, analyze, and understand visual inputs from the physical world (such as factories and other industrial sites) to help humans take proper actions and assist them in various production processes.
Rely on Itransition's computer vision consulting and implementation services to fully seize the benefits of this technology in your business scenario.
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
Cybersecurity
Advanced data analytics including predictive/prescriptive analytics
Automation/robotics
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: ibm.com — 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
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.
Actuator
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.
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.
1
Business needs analysis
Analyze customer business needs and expectationsÂ
Assess the current tech ecosystem
Frame corporate needs and goals
Define the solution’s scope and requirements
2
Initial data analysis
Map and assess corporate data sources
Identify external data sources (public databases, etc.)
3
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
4
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
5
Integration and rollout
Integrate the CV model into the solution
Deploy the product in the target environment
6
Support
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.
Integration
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.
Processing
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.
Security
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
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.
