NIST researchers found that using certain masks (A and C) to mark data helped train a neural network model to detect small, geometric objects within dense, low-quality plots. The purpose of the project was to recover the lost data in journal articles, but this type of object detection also has other applications such as image analyses, self-driving cars, surveillance and machine inspections.
In efforts to automatically capture important data from scientific papers, computer scientists at the National Institute of Standards and Technology (NIST) have developed a method to accurately detect small, geometric objects such as triangles within dense, low-quality plots contained in image data. Employing a neural network approach designed to detect patterns, the NIST model has many possible applications in modern life.
NIST’s neural network model captured 97% of objects in a defined set of test images, locating the objects’ centers to within a few pixels of manually selected locations.
“The purpose of the project was to recover the lost data in journal articles,” NIST computer scientist Adele Peskin explained. “But the study of small, dense object detection has a lot of other applications. Object detection is used in a wide range of image analyses, self-driving cars, machine inspections, and so on, for which small, dense objects are particularly hard to locate and separate.”
The researchers took the data from journal articles dating as far back as the early 1900s in a database of metallic properties at NIST’s Thermodynamics Research Center (TRC). Often the results were presented only in graphical format, sometimes drawn by hand and degraded by scanning or photocopying. The researchers wanted to extract the locations of data points to recover the ..