This study is devoted to the development of methods for the automatic assessment of the granulometric composition of ore after blasting based on data obtained from unmanned aerial vehicles (UAVs). Determining the size of ore fragments is an important step, since the effectiveness of subsequent crushing processes depends on its accuracy. Traditional methods of analysis use manual work, which requires considerable labor and is subject to subjective factors. The study examines modern machine learning methods and neural network architectures, such as Feature Pyramid Network (FPN), EfficientNet and SE ResNet, which can automatically and accurately segment images. As a result of the experiments, it was found that the VPN network with a pre-trained EfficientNet B2 base showed the highest IoU accuracy among the models.

Keywords: granulometric composition, FPN, Efficient Net, SEResNet