НЕЧЕТКОЕ ИЗМЕРЕНИЕ КООРДИНАТ ОБЪЕКТОВ МАЛОГО РАЗМЕРА НА СНИМКАХ ВЫСОКОГО РАЗРЕШЕНИЯНЕЧЕТКОЕ ИЗМЕРЕНИЕ КООРДИНАТ ОБЪЕКТОВ МАЛОГО РАЗМЕРА НА СНИМКАХ ВЫСОКОГО РАЗРЕШЕНИЯ

Abstract

The article considers the solution of the problem of fuzzy measurement of the coordinates of small objects occupying less than 1% of the area in highresolution images. The EfficientNet and MobileNet classifiers from the Tensorflow library, pre-trained on ImageNet data, are used as the basis of the algorithm for extracting features of small objects. Next, the feature map from the last layers of the neural network is fed to the input of a two-layer perceptron, which implements classification by the feature vector in depth, for each element along the X and Y coordinate axes. The results are interpreted as a measure of the presence of an object in the receptive field in the original image. Thus, the authors have significantly simplified the architecture of the solution, while achieving acceptable indicators of accuracy and precision – 68 and 85%, respectively, the inference time on mobile platforms is less than 1 second.