Calibration Method for Stereovision Measurement of High-Temperature Components Using Two Infrared Cameras

摘要

The infrared vision measurement method has some advantages over visible vision in the measurement of high-temperature components. Infrared imaging is based on different imaging principles, however, making it unreasonable to adopt a conventional visible-light camera calibration method directly. The present study proposes a dual infrared-camera calibration program in which we use the infrared imaging principle to measure high-temperature components three-dimensionally. We use two ceramic balls as calibration targets against the background of an external high-temperature radiation source. Multiple feature points are generated from the precise movement of these targets. In order to improve the accuracy of the calibration method, we took the following approaches: A highly accurate edge-detection algorithm is realized by using a fuzzy neural network that is self-learning, self-adaptive, and utilizes fuzzy processing. We thus achieve a low signal-to-noise ratio and low contrast in infrared images. The distance between these two ceramic targets is used as a calibration reference to further reduce the temperature effect and to improve calibration accuracy and efficiency. The calibration results we got are an average residual error of 6.4134 μm and a variance of 2.9205 μm.