Розробка методу сегментування оптико-електронного зображення з безпілотного літального апарату на основі мурашиного алгоритму в умовах впливу шуму сіль та перець
Year: 2025
Type: article
Abstract: The object of research is the process of segmenting an image from an unmanned aerial vehicle based on the ant algorithm under the influence of salt-and-pepper noise. “Salt”-and-“pepper” noise occurs due to data transmission errors, failures of digital camera sensors or malfunctions during recording/reading of information. It is characterized by the random appearance of individual pixels in the image, the value of which is equal to the minimum (“pepper”) or maximum (“salt”) brightness level. Unlike the known ones, the method of segmenting an optoelectronic image based on the ant algorithm provides image segmentation under the influence of salt-and-pepper noise and involves: – initialization of initial parameters; – calculation of the length of the path segment of agents; – calculation of the attractiveness of the route for the agent; – updating the pheromone concentration; – calculation of the probability of transition of agents; – calculation of the objective function; – movement of agents; – determination of the best route of agents. Experimental studies have shown that the segmentation method based on the ant algorithm provides a reduction in segmentation errors of the first kind on average: – in the absence of salt-and-pepper noise – 4%; – at the intensity of salt-and-pepper noise σ = 5–21%; – at the intensity of salt-and-pepper noise σ = 15–10%. The segmentation method based on the ant algorithm provides a reduction in segmentation errors of the second kind on average: – in the absence of salt-and-pepper noise – 3%; – at the intensity of salt-and-pepper noise σ = 5–15%; – at the intensity of salt-and-pepper noise σ = 15–6%. The practical significance of the segmentation method based on the ant algorithm is to ensure high-quality image segmentation under the influence of salt-and-pepper noise.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7117942957
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