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Underwater Vision Technologies for Smart Fisheries: A Comprehensive Review of OpenCV-Based Optimization and Edge Computing Applications
- 1 Dundee International Institute, Central South University, Chang Sha, Hunan Province, Country, 410006
* Author to whom correspondence should be addressed.
Abstract
With the deepening exploration of marine resources and the global emphasis on sustainable development, intelligent fishery has emerged as a critical domain for advancing ecological conservation and operational efficiency. Underwater vision technology, a cornerstone of intelligent fishery systems, encounters substantial challenges due to complex underwater environments—such as light attenuation, turbidity, biofouling, and dynamic currents—which degrade image quality and impede real-time decision-making. To address these limitations, this paper systematically reviews the integration of OpenCV-based image processing techniques with edge computing frameworks, which collectively enhance the robustness and adaptability of underwater visual systems. OpenCV’s advanced algorithms, including Contrast-Limited Adaptive Histogram Equalization for low-light enhancement, geometric transformations for distortion correction, and YOLO-based object detection, have been shown to significantly improve image clarity and target recognition accuracy. Simultaneously, edge computing alleviates latency and bandwidth constraints by enabling real-time data processing on embedded devices, achieving sub-200 ms response times for critical tasks such as dissolved oxygen monitoring and fish behavior analysis. Field validations underscore performance improvements, such as 92% recognition accuracy in coral reef monitoring and 85% mean Average Precision for aquatic species detection using MobileNet-SSD models. Despite these advancements, challenges remain in extreme conditions, computational resource optimization for edge devices, and the need for interdisciplinary collaboration to integrate marine biology insights into algorithmic design. Future research directions highlight hybrid architectures combining physics-based restoration with quantized deep learning, bio-inspired optical sensors, and socio-technical frameworks to ensure equitable technology adoption.
Keywords
OpenCV, edge computing, intelligent fishery, underwater vision, image processing technology
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Cite this article
Lv,H. (2025). Underwater Vision Technologies for Smart Fisheries: A Comprehensive Review of OpenCV-Based Optimization and Edge Computing Applications. Applied and Computational Engineering,151,1-9.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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