Research of Convolutional Neural Networks in the Problem of Classification of Images of Radioactive Waste
Radioactive contamination of the environment always occurs very quickly and on a large scale. At the same time, the causes of infection may be different and there is no guarantee that they will not occur again. Therefore, it is always necessary to be ready to take emergency measures to eliminate the consequences of radioactive contamination. To date, the most effective and safe solution to the problem of the elimination of radioactive waste is burial, therefore it is necessary to ensure the compactness of the placement of radioactive waste. To do this, contaminated waste can be compressed or burned in special installations. The use of vision systems with the use of convolutional neural networks will allow classifying the material of radioactive waste to determine incinerated or pressed objects. To conduct experiments on the applicability of CNN for this task, a dataset was formed containing images from open datasets, as well as their own images. The conducted experiments confirm the effectiveness of using deep convolutional neural networks for this task. The best result of the classification of the material was shown by the InceptionV3 network with an accuracy of 89,58%. The greatest efficiency can be achieved by using the predictions of several neural networks, with the help of which the accuracy has increased to 91,58%.