A Comprehensive Framework for Underwater Object Detection Based on Improved YOLOv8

Authors

  • Victor Sineglazov National Aviation University, Kyiv https://orcid.org/0000-0002-3297-9060
  • Mykhailo Savchenko National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,”

DOI:

https://doi.org/10.18372/1990-5548.79.18429

Keywords:

underwater object detection, classification problem, YOLO, hybrid neural networks, deep learning

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 82.67% precision, 81.02 % recall, and 86.3 % 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.

Author Biographies

Victor Sineglazov , National Aviation University, Kyiv

Doctor of Engineering Science

Professor

Head of the Department of Aviation Computer-Integrated Complexes

Faculty of Air Navigation Electronics and Telecommunications

Mykhailo Savchenko , National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,”

Master’s degree student

Artificial Intelligence Department

Educational and scientific Institute Applied System Analysis

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Published

2024-03-29

Issue

Vol. 1 No. 79 (2024)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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