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 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
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)
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
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
Provides the sensing module with uniform illumination and enables it to capture clear images of the manufacturing system.
Encompasses assembly lines, assembly robots, and automated guided vehicles.
Comprises cameras that collect visual data from the manufacturing system and send it to the CV system via the Internet of Things (IoT).
Typically powered by machine learning (ML) algorithms, it can perform different tasks from product counting to execution quality control with defect (anomaly) detection.
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.
Computer vision systems can be deployed in many use cases and operational scenarios within manufacturing processes.
Here are a few real-life examples of manufacturing companies leveraging computer vision in a variety of processes:
The adoption of computer vision systems in industrial processes can strengthen the manufacturing sector in terms of:
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.
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.
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.
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.
When implementing computer vision in manufacturing, you can face some technical challenges. Keep in mind the following recommendations to streamline its adoption.
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.
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