A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor’s field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes’ ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna’s Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing.

中文翻译：

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结合了声光传感器的蓝鳍金枪鱼自动上浆。

描述了一种针对水下传感器的建议，该水下传感器将声学装置与光学装置相结合以从腹侧角度自动调整幼年蓝鳍金枪鱼的大小。当金枪鱼自由游泳并且鱼穿过我们组合传感器的视场时，将获得声音和光学信息。图像处理技术用于识别和分类声学数据中的鱼类痕迹（回波图），而视频帧则通过拟合鱼类腹侧轮廓的可变形模型进行处理。最后，一旦验证了两种数据之间的对应关系，就可以将处理后的声学和光学数据组合起来，对鱼进行大小调整。所提出的系统能够自动对金枪鱼的吻叉长度（SFL）和宽度进行精确测量。Thunnus thynnus），而不会影响测量的准确性。这项工作验证了将声音和光学数据相结合以进行鱼类定型的程序，这是嵌入式传感器的第一步，嵌入式传感器的电子和处理能力应进行优化，以在电源方面实现自主并实现实时处理。