2022 Volume 34 Issue 2 Pages 533-538
When a Convolutional Neural Network (CNN) classifier with the input of a small patch image evaluates the state of rust on weathering steel, the evaluation result depends on where the patch image is cut out. In this paper, we propose two types of methods to improve rust state evaluation using CNN. Both types employ multiple CNN classifiers based on bagging, which is one of ensemble learning. The final judgment result is derived by integrating the judgment results of multiple CNN classifiers. The first method is a method for improving the estimation accuracy of each patch image, and the integration of judgment utilizes majority voting and judgment probability. The second method is an evaluation method for a large tape image. Its evaluation result is derived by using a plurality of patch images cut out from the large tape image as input of CNN classifiers. Numerical experiments show the effectiveness of the proposed method.