Abstract: A method and system for remote visual inspection and failure detection at industrial sites, utilizing pre-existing digital cameras connected to a server. The server hosts adaptive object-detection software that analyzes images from the cameras to identify focus cues, which indicate areas prone to failure. The system contextualizes these images to define interest regions that require ongoing monitoring. In a continuous monitoring loop, new images are periodically received, processed, and analyzed for visible failure attributes. Detected potential failures trigger notifications to users via a human interface, allowing them to validate or modify the system's findings. User feedback is used to enhance the software's accuracy over time. The system supports various camera types and is adaptable to multiple monitoring environments, offering a robust solution for automating failure detection without requiring specialized site hardware.

Abstract: A method of remote facilities monitoring for the detection of contamination, leaks or failures in petrochemical and related facilities. Remote cameras are used to capture images of equipment to be inspected, which are transmitted to a central server. A software component on the central server applies artificial intelligence and image processing methods to detect the presence of anomalies in the images for display to an operator and potential subsequent dispatch of in person follow-up. Parallel data streams from sensors at the remote sites can be used by the software on the server to enhance the level of confidence in anomaly detection. The server software uses an adaptive object-detection function to analyze image data and learns over time to provide enhanced detection of interest regions and failure conditions.