Oracle’s AI vision centers on automated defect detection for digital twins, leveraging Oracle Cloud Infrastructure (OCI) Vision. This is a great supporting ally to implement defect detection in the automation of its analysis and an example of an advanced AI service in image processing on a large scale with the aid of deep learning, which provides pre-built models for image and text recognition in an automated way.
OCI Vision plays a pivotal role in automating defect detection in construction scenarios. Raw data is gathered by drones inspecting specific areas of a construction project, and the images are uploaded to OCI Object Storage. An event trigger then initiates the OCI Vision service, which employs custom models to detect anomalies within the collected images.
In the recent demonstration, OCI Vision accurately identified a rust-damaged girder, exemplifying its ability to pinpoint visual defects. It was not only able to detect anomalies in the service but also determine the type of defect and save it in the log for review. Such a workflow goes on without interruption, starting from data sources collected and uploaded into the OCI Object Storage, processed by OCI Vision, and returned to trigger the output of a ticket with the findings. This automated process not only saves time but also helps prevent potentially disastrous and costly scenarios.
Beyond construction, OCI Vision’s capabilities extend to diverse industries, enabling developers to train custom vision models using their own datasets for specialized applications. These models can organize digital media assets, tag items in images for tasks like product counting or shipment tracking, and detect visual anomalies in manufacturing processes.
With its cutting-edge AI capabilities and seamless integration, OCI Vision empowers organizations to unlock the full potential of digital twins, automating defect detection and analysis with unparalleled efficiency and accuracy.
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Featured image: Credit: Oracle
