Computer Vision for Industry
Boost your operational efficiency by leveraging real-time data from videos and images.
In the manufacturing sector, computer vision has become integral to automation solutions. This technology drives advancements in quality control, safety, and production efficiency. Here are some key computer vision applications that enhance manufacturing processes:
Defect Detection
Achieving 100% accuracy in defect detection remains a significant challenge for large-scale manufacturing operations.
Our advanced camera systems capture real-time visual data and apply sophisticated computer vision and machine learning algorithms to analyze and compare this data against established quality benchmarks. This process allows for the precise detection of both large and small-scale defects throughout the production line. By improving the accuracy of defect identification, our technology not only supports a flawless production process but also helps reduce overall costs.
Reading text and barcodes
As most products have barcodes on their packaging, a computer vision technique called OCR can be successfully applied to automatically detect, verify, convert and translate barcodes into readable text.
By applying OCR to photographed labels or packaging, the text they contain is extracted and verified against databases. This procedure helps to identify wrongly labeled products, provide information about expiration dates, inform about product quantity in the magazine, and track packages at all stages of product development.
Product assembly
World-class companies have already implemented automation of the product assembly line—the company reported the automation of over 70% of their manufacturing processes. Computer vision generates 3D modeling designs, guides robots and human workers, identifies and tracks product components, and helps to maintain packaging standards.
Predictive maintenance
In the manufacturing environment, it’s common to see material degradation and corrosion causing equipment deformation. If not handled properly, this process can halt the manufacturing lines and compromise ’workers’ safety. Computer vision helps to monitor the machinery and equipment to find the maintenance needs and address them proactively before it’s too late.
Computer vision-powered devices monitor incoming data from machinery through cameras that identify defects and other changes. When they detect an issue, they send a signal to the system, allowing human operators to take corrective action before an asset is damaged or an accident occurs.
Aerial survey and imaging
Scene and environment monitoring using drones has become an essential element of agricultural transformation. Farmers can now leverage image data captured by drone cameras and processed using computer vision for remote crop and livestock monitoring and collect information about field geography or soil composition.
