The limitations imposed by traditional sampling methods have restricted the acquisi- tion of data on key fisheries parameters. This is particularly the case for juveniles because most traditional gear explicitly avoids the capture of juveniles, and the juveniles of many species use habitats in which traditional gear is ineffective. The increasing availability and sophistication of Remote Underwater Video Techniques (RUVs) such as Baited Remote Underwater Video, Unbaited Remote Underwater Video and Remotely Operated Underwater Vehicles offer the opportunity of over- coming some of the key limitations of more traditional approaches. However, RUV techniques come with their own set of limitations that need to be addressed before they can fully realize their potential to shed new light on the early life history of fish. We evaluate key strengths and limitations of RUV techniques, and how these can be overcome, in particular by employing bespoke computer vision Artificial Intelligence approaches, such as Deep Learning in its Convolutional Neural Networks instantia- tion. In addition, we investigate residual issues that remain to be solved despite the advances made possible by new technology, and the role of explicitly identifying and evaluating key residual assumptions.

中文翻译：

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优化幼鱼调查的视频采样：使用深度学习和假设评估来产生关键的渔业参数

传统抽样方法的局限性限制了关键渔业参数数据的获取。对于幼鱼来说尤其如此，因为大多数传统渔具明确避免捕获幼鱼，而且许多物种的幼鱼使用传统渔具无效的栖息地。远程水下视频技术 (RUV) 的可用性和复杂性不断提高，例如有诱饵的远程水下视频、无诱饵的远程水下视频和远程操作的水下车辆，为克服更传统方法的一些关键限制提供了机会。然而，RUV 技术有其自身的一系列局限性，需要先解决这些局限性，然后才能充分发挥其为鱼类早期生活史提供新思路的潜力。我们评估了 RUV 技术的主要优势和局限性，以及如何克服这些优势和局限性，特别是通过采用定制的计算机视觉人工智能方法，例如卷积神经网络实例中的深度学习。此外，我们调查了尽管新技术取得了进步，但仍有待解决的剩余问题，以及明确识别和评估关键剩余假设的作用。