Мережа для виявлення підводних об’єктів з використанням модифікованої архітектури YOLOv8
DOI:
https://doi.org/10.18372/1990-5548.79.18429Ключові слова:
виявлення підводних об’єктів, класифікація, YOLO, гібридні нейронні мережі, глибоке навчанняАнотація
В даній роботі розроблено нейронну мережу для виявлення підводних об’єктів на основі модифікованої архітектури YOLOv8. Розглянуто використання модуля попередньої обробки зображень на основі контрастно-обмеженого адаптивного вирівнювання гістограми, архітектури GhostNetV2 для ефективного вилучення ознак і зменшення загальної кількості параметрів, механізму уваги Coordinate Attention та оператора Deformable ConvNets v4 для покращеної репрезентації ознак. Модель перевірено на вибірці UTDAC2020 (результати – precision 82.67%, recall 81.02%, mAp 86.3% при значенні IoU = 0.5), що випереджає результати YOLOv8s на даній вибірці при зменшенні обчислювальної складності на 15.1%. Результат даної роботи можна застосувати для розробки програмного забезпечення для безпілотних підводних апаратів.
Посилання
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