Informacionnye TehnologiiInformacionnye TehnologiiИнформационные технологии1684-6400New Technologies Publishing House70234310.17587/it.32.37-45Digital processing of signals and imagesЦифровая обработка сигналов и изображенийResearch ArticleInformative content evaluation of wild animal images based on production rulesОценка информативности контента изображений диких животных на основе продукционных правилFavorskayaM. N.ФаворскаяМ. Н.
Russian Federation

Dr. Tech. Sc., Professor

д-р техн. наук, проф.

favorskaya@sibsau.ruNatalenkoD. N.НаталенкоД. Н.
Russian Federation

PhD Student

аспирант

dmitriy.natalenko@mail.ruReshetnev Siberian State University of Science and Technology named after Academician M. F. ReshetnevСибирский государственный университет науки и технологий имени акад. М. Ф. Решетнева1501202632137450802202608022026Copyright ©; 2026, Informacionnye TehnologiiCopyright ©; 2026, Информационные технологии2026Informacionnye TehnologiiИнформационные технологииhttps://journals.eco-vector.com/1684-6400/article/view/702343

The wild animal images captured by camera traps often have different quality due to such artifacts as low lighting conditions, complex background, meteorological conditions, the use of low-resolution video cameras, etc. А modern solution to the problem of recognizing wild animals is the use of deep learning models that need to be trained on "good" examples. Thus, assessing the informative content of such images is in the scope of interest. Image quality factors (brightness, contrast, blurriness and weather conditions), as well as the shape and position of the animal relative to the camera trap, are taken into account. Production rules have been developed for making decisions about dividing images into classes of varying informative degrees of information content. The experiments were carried out using a data set collected in the Ergaki Natural Park, Krasnoyarskiy Kray, in 2012-2021. The average error value of the proposed method for all classes is 6.4 % relative to the expert assessment.

Исходные изображения диких животных, полученные от фоторегистраторов, нередко имеют разное качество из-за артефактов освещения, метеорологических условий, использования видеокамер низкого разрешения и т. д. Современным решением проблемы распознавания диких животных является использование моделей глубокого обучения, которые необходимо обучать на "хороших" примерах. Таким образом, оценка информативности контента изображений является актуальной. Учитываются факторы, влияющие на качество изображений (освещение, размытость, наличие шума и погодные условия), а также форма и положение животного относительно фоторегистратора. Разработаны продукционные правила для принятия решения о разделении изображений на классы разной степени информативности. Эксперименты проводили с использованием набора данных, собранного в природном парке "Ергаки" Красноярского края в 2012—2021 годах. Среднее значение ошибки предлагаемого метода по всем классам составляет 6,4 % относительно экспертных оценок.

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