Identification, Recognition, and Classification of Micro-Objects Based on Signal-Point Characteristics of an Image

A problem was formulated, and methods and algorithms for identifying, recognizing, and classifying micro-objects were developed using problem-oriented image processing systems utilizing the characteristics of image structural components, dynamic models, and neural networks. The micro-objects studied were pollen grains and unicellular microorganisms, the application of which is in demand in palynology, environmental protection, ecology, and medicine. Models, algorithms, and software were developed based on the statistical, dynamic, morphometric, textural, and brightness characteristics of point image signals. The main contribution of the work is a hybrid model combining Daubechies wavelet functions (orders 4 and 8) with convolutional neural networks, which provides an average pollen grain recognition and classification accuracy of up to 97.7% and an overall classification accuracy of 98.2%. For comparative evaluation, the YOLO11m model was used as an independent baseline deep learning model, achieving 88.6% overall accuracy on a validation set of 280 images. The software includes tools for Gaussian filtering, median filtering, Sobel and Canny operators, and threshold correction of defective points with hard and soft control rules. The software package includes modules for training a convolutional neural network. The study was conducted on real-world databases of micro-object images for crop breeding and environmental pollution assessment.