Layer-by-layer Learning In Image Processing For Unmanned Aerial Vehicle

This work explores an innovative approach to image processing that provides high efficiency and accuracy in computer vision tasks. In this work, step-by-step learning of quantum machine learning models is considered, in which each layer of the model specializes in specific functions, which significantly improves the quality of image recognition and classification. Special attention is paid to the adaptation of this approach to real-time conditions important for UAVs, where the processing of large volumes of data must take place under conditions of limited resources. The results of experiments are presented, which demonstrate the advantages of step-by-step learning in comparison with traditional methods of image processing, which allows to achieve higher accuracy and speed of data analysis during various tasks, such as monitoring, mapping and object detection in the field.