Industries have a lot of infrastructure. They range from thousands of miles of pipelines in the oil and gas industry to enormous acres of land in agriculture, not to mention the colossal transportation structures, such as roads, bridges and railway tracks. These structures, along with multitude of industrial equipment, need to be inspected and maintained so that these industries can continue to operate. Corporations employ various methods to do this, such as routine visual inspections, running tests and digging deeper. However, with the huge number of structures and equipment to keep track, many inefficiencies exist.
Visual inspection can prevent disaster and maintain seamless operations. In most cases a quick look can identify whether there is a defect, and with some experience we can identify what the defect is. With the Watson Visual Recognition service, we can determine whether the equipment in the image meets normal conditions or by training the service to identify particular defects and damage.
In our code pattern, Industrial Visual Analysis, we’ll train the Watson Visual Recognition service to inspect oil and gas pipelines to classify the image into categories: Normal, Burst, Corrosion, Damaged Coating, Joint Failure and Leak. The training is performed by providing the service with a set of images for each category, by defining a particular classifier. This classifier can then analyze the image and provide a percent match to each category.
In our code pattern, we have set up the Visual Recognition analysis with IBM Cloud Functions and a Cloudant database. When a user uploads an image, the initial image data is stored in our Cloudant database, which triggers a microservice using IBM Cloud Functions to analyze the image. Once the analysis is complete, the database is updated with the Visual Recognition analysis data. The pattern includes a web application, which uses the analysis data to display a dashboard for the images that require immediate attention to images needing no attention.
Currently the web interface can be used by the user to upload images. However with the setup of Cloudant and IBM Cloud Function, this pattern can be extended to capture images from external sources and devices, including drones, cameras on location, satellite images and more. So depending on the industrial use case, developers can use the Watson Visual Recognition service to automate the inspection process!