Low-Cost Underwater Camera: Design and Development

被引:1
|
作者
Dadios, Elmer P. [1 ,2 ]
Almero, Vincent Jan [3 ]
Concepcion, Ronnie S., III [1 ,2 ]
Vicerra, Ryan Rhay P. [1 ,2 ]
Bandala, Argel A. [2 ,3 ]
Sybingco, Edwin [2 ,3 ]
机构
[1] De La Salle Univ DLSU, Dept Mfg Engn & Management, 2401 Taft Ave, Manila 1004, Philippines
[2] De La Salle Univ DLSU, Ctr Engn & Sustainabil Dev Res, 2401 Taft Ave, Manila 1004, Philippines
[3] De La Salle Univ DLSU, Dept Elect & Comp Engn, 2401 Taft Ave, Manila 1004, Philippines
关键词
additive manufacturing; computational intelligence; fish monitoring; underwater camera;
D O I
10.20965/jaciii.2022.p0851
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The understanding of vision-based data acquisition and processing aids in developing predictive frame-works and decision support systems for efficient aqua-culture monitoring and management. However, this emerging field is confronted by a lack of high-quality underwater visual data, whether from public or local setups and high cost of development. In this regard, an underwater camera that captures underwater images from an inland freshwater aquaculture setup was proposed. The components of the underwater camera system are primarily based on Raspberry Pi, an open-source computing platform. The underwater camera continuously provides a real-time video streaming link of underwater scenes, and the local processor periodically acquires and stores data from this link in the form of images. These data are stored locally and remotely. Based on the results of the developed low-cost underwater camera, it captures and differentiate fish region to its background before and after flushing as influenced by turbidity. Hence, the developed camera can be used for both aquarium and inland aquaculture pond setup for fish monitoring.
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页码:851 / 858
页数:8
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