OpenTalks.AI - Никита Андриянов, Анализ эффективности распознавания образов на нестандартных типах изображений на примере радиолокационных изображений местности и рентгеновских снимков багажа и ручной клади
OpenTalks.AI - Никита Андриянов, Анализ эффективности распознавания образов на нестандартных типах изображений на примере радиолокационных изображений местности и рентгеновских снимков багажа и ручной клади
40-36-25 Grayscale 112x112x1 8 SGD 62.34% CNN 40-36-25 Color 112x112x3 8 SGD 66.48% CNN 64-48-32 Color 112x112x3 200 ADAM 69.32% CNN 40-36-25 Color 112x112x3 8 ADAM 69.62% CNN 64-48-32 Color 112x112x3 8 ADAM 75.32% CNN 64-48-32 Color 112x112x3 100 ADAM 81.75% CNN 64-48-32 Color 128x128x3 100 ADAM 85.71% CNN 128-128-32 Color 128x128x3 50 ADAM 90.47%
the problem of classifying radar images containing 3 types of objects was successfully solved: an airplane, a road, a ship. To improve the quality of the neural networks, a modification of the gradient descent algorithm and data augmentation were used. For most of the images, the network managed to achieve "confidence in its choice" over 90%. 2) Using the example of primitive modifications of the input data and the architecture of a neural network for X-ray scanning images of airport security services, a neural network was trained on a low-performance computer, which provided a result of more than 90% on a test sample. 3) The study of standard regularizations during training on the data of X-ray images showed general trends with the processing of optical images. Obviously, the accuracy in this case is directly proportional to the depth of the network. However, over time, a further increase in the number of layers will lead to a much larger increase in computational costs than a gain in recognition accuracy. Interestingly, resetting the weights in this problem led to a significant decrease in the recognition accuracy. At the same time, the maximum accuracy for augmentations was achieved at a probability of 0.2. At the same time, for the considered example, resetting the weights did not lead to an improvement in the quality of the network, and insignificant augmentation increased the accuracy by about 3.8%.
Russian Foundation for Basic Research and the Government of the Ulyanovsk Region, within the framework of Project No. 19-47-730011 Also special thanks to the organizers of the conference OpenTalks.AI-2021
Nikita Andriyanov, PhD (candidate of technical sciences), Assistant Professor, Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation n.andriyanov nikita-and-nov@mail.ru