Investigation of the Capabilities of Artificial Neural Networks in the Problem of Classifying Objects with Dynamic Features

Image classification is a classic machine learning (ML) problem. Neural networks are widely used in the problem of object classification. Despite the existence of a large number of image classification algorithms, very little attention is paid to the issue of video data classification. In the case of using convolutional neural networks to classify frames of a video sequence, it is necessary to combine image features to obtain a prediction. However, with this approach, the signs of object dynamics will be ignored, since the images are processed sequentially. Therefore, the issue of analyzing objects with dynamically changing characteristics remains relevant. To solve this issue, the authors propose to use a neural network with long-term short-term memory (LSTM). In contrast to classical convolutional neural networks (CNN), the proposed network uses information about the sequence of images, thereby providing a higher classification accuracy of detected objects with dynamic characteristics. In the study, the authors analyze the classification accuracy of smoke cloud detection in a forest using various machine learning methods. In the work, the authors present models for the classification of one frame and a sequence of frames of a video sequence. The results of the work of machine learning models are presented, as well as a comparative analysis of the classification of one frame and a sequence of frames. The accuracy of the video sequence classification by the model of a recurrent neural network with an LSTM layer was 85.7%.