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https://er.nau.edu.ua/handle/NAU/63103| Title: | A Comprehensive Framework for Underwater Object Detection Based on Improved YOLOv8 |
| Other Titles: | Мережа для виявлення підводних об’єктів з використанням модифікованої архітектури YOLOv8 |
| Authors: | Sineglazov, Victor Синєглазов, Віктор Михайлович |
| Keywords: | underwater object detection classification problem YOLO hybrid neural networks deep learning виявлення підводних об’єктів класифікація YOLO гібридні нейронні мережі глибоке навчання |
| Issue Date: | 29-Mar-2024 |
| Publisher: | National Aviation University |
| Citation: | Sineglazov V. M. A Comprehensive Framework for Underwater Object Detection Based on Improved YOLOv8 / V. M. Sineglazov, M. V. Savchenko // Electronics and Control Systems. Kyiv: NAU, 2024. – No 1(79). – pp. 9–15. |
| Series/Report no.: | Electronics and Control Systems;№1(79) Електроніка та системи управління;№1(79) |
| Abstract: | Underwater object detection poses unique challenges due to issues such as poor visibility, small densely packed objects, and target occlusion. In this paper, we propose a comprehensive framework for underwater object detection based on improved YOLOv8, addressing these challenges and achieving superior performance. Our framework integrates several key enhancements including Contrast Limited Adaptive Histogram Equalization for image preprocessing, a lightweight GhostNetV2 backbone, Coordinate Attention mechanism, and Deformable ConvNets v4 for improved feature representation. Through experimentation on the UTDAC2020 dataset, our model achieves 82.35% precision, 80.98 % recall, and 86.21 % mean average precision at IoU = 0.5. Notably, our framework outperforms the YOLOv8s model by a significant margin, while also being 15.1% smaller in terms of computational complexity. These results underscore the efficiency of our proposed framework for underwater object detection tasks, demonstrating its potential for real-world applications in underwater environments. 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 82.35%, recall 80.98%, mAp 86.21% 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. |
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| URI: | https://er.nau.edu.ua/handle/NAU/63103 |
| ISSN: | 1990-5548 |
| DOI: | 10.18372/1990-5548.79.18429 |
| Appears in Collections: | Наукові публікації та матеріали кафедри авіаційних комп'ютерно-інтегрованих комплексів (НОВА) |
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