In recent years, unmanned aerial vehicles (UAVs) have been used in different areas of applications such as rescue operations, surveillance, agriculture, aerial mapping, engineering applications and research, among others, in order to perform tasks with greater efficiency. This work focuses on the use of UAVs in the fishing sector in order to optimise the detection process of a shoal of fish. In this sense, the main idea is to perform images recognition using the images acquired through videos captured by UAV in the open sea; to achieve the objective the convolutional neural networks were used, a new dataset with different images captured through UAV videos in the open sea were taken into account, these classes correspond to dolphin, dolphin_pod, open_sea, and seabirds. The training tests were by transfer of learning using the following models: Inception V3, MobileNet V2, and NASNet-A (large) trained on TensorFlow platform. The experimental results show the detection performance with high-precision values in reasonable processing time. This study ends with a critical discussion of the experimental results.

中文翻译：

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使用卷积神经网络的无人机视频图像识别

近年来，无人驾驶飞机（UAV）已用于不同的应用领域，例如救援行动，监视，农业，空中测绘，工程应用和研究等，以更高效地执行任务。这项工作着重于在渔业部门使用无人机，以优化鱼群探测过程。从这个意义上讲，主要思想是使用通过无人机在公海拍摄的视频获取的图像进行图像识别；为了达到使用卷积神经网络的目的，考虑了一个新的数据集，该数据集具有通过公海中的无人机视频捕获的不同图像，这些类别分别对应于海豚，dolphin_pod，open_sea和海鸟。通过使用以下模型的学习转移来进行培训测试：在TensorFlow平台上培训了Inception V3，MobileNet V2和NASNet-A（大型）。实验结果表明，该算法在合理的处理时间内具有较高的检测精度。这项研究以对实验结果的批判性讨论结束。