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Обнаружение объектов на изображении: от критериев Байеса и Неймана–Пирсона к детекторам на базе нейронных сетей EfficientDet
Н.А. Андриянов 1, В.Е. Дементьев 2, А.Г. Ташлинский 2

1 Финансовый университет при Правительстве Российской Федерации,
125993, Россия, г. Москва, Ленинградский пр-т, д. 49;

2 Ульяновский государственный технический университет,
432027, Россия, г. Ульяновск, ул. Северный Венец, д. 32

 PDF, 2226 kB

DOI: 10.18287/2412-6179-CO-922

Страницы: 139-159.

Аннотация:
Актуальность задач обнаружения и распознавания объектов на изображениях и их последовательностях с годами только возрастает. За последние несколько десятилетий предложено огромное количество подходов и методов обнаружения как аномалий, то есть областей изображения, характеристики которых отличаются от прогнозных, так и объектов интереса, о свойствах которых есть априорная информация, вплоть до библиотеки эталонов. В работе предпринята попытка системного анализа тенденций развития подходов и методов обнаружения, причин этого развития, а также метрик, предназначенных для оценки качества и достоверности обнаружения объектов. Рассмотрено обнаружение на основе математических моделей изображений. При этом особое внимание уделено подходам на основе моделей случайных полей и отношения правдоподобия. Проанализировано развитие сверточных нейронный сетей, направленных на задачи распознавания и обнаружения, включая ряд предобученных архитектур, обеспечивающих высокую эффективность при решении данной задачи. В них для обучения используются уже не математические модели, а библиотеки реальных снимков. Среди характеристик оценки качества обнаружения рассмотрены вероятности ошибок первого и второго рода, точность и полнота обнаружения, пересечение по объединению, интерполированная средняя точность. Также представлены типовые тесты, которые применяются для сравнения различных нейросетевых алгоритмов.

Ключевые слова:
распознавание образов, обнаружение объектов, компьютерное зрение, обработка изображений, случайные поля, CNN, IoU, mAP, вероятность правильного обнаружения.

Благодарности
Исследование выполнено при финансовой поддержке РФФИ в рамках научного проекта № 20-17-50020 и частично проекта №19-29-09048.

Цитирование:
Андриянов, Н.А. Обнаружение объектов на изображении: от критериев Байеса и Неймана–Пирсона к детекторам на базе нейронных сетей EfficientDet / Н.А. Андриянов, В.Е. Дементьев, А.Г. Ташлинский // Компьютерная оптика. – 2022. – Т. 46, № 1. – С. 139-159. – DOI: 10.18287/2412-6179-CO-922.

Citation:
Andriyanov NA, Dementiev VE, Tashlinskiy AG. Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks. Computer Optics 2022; 46(1): 139-159. DOI: 10.18287/2412-6179-CO-922.

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