Overfishing of marine fishery is a serious threat to fishery ecological security. Fishing regulation is one of the main ways to maintain marine fishery ecology. In order to improve the intelligence of fishing supervision system, a real time fish detection method based on YOLO-V3-Tiny-MobileNet wasproposed. Aiming at the problems of shallow network layers and insufficient feature extraction ability in YOLO-V3-Tiny network, the proposed network takes YOLO-V3-Tiny as baseline and combines it with MobileNet. The proposed network is pre-trained by VOC2012 dataset, and then retrained and tested on Kaggle_ NCFM (The Nature Conservancy Fisheries Monitoring) dataset. The experimental results show that the proposed method has superior performance in parameters number, mean average precision and detection performance, compared with other methods. Compared with the monitoring method of fishing vessel detection on shore supervision, the real time monitoring method can give timely warning to the fishing vessel operators, which is more conducive to fishery ecological protection.

中文翻译：

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海洋渔业监管现场基于深度神经网络的实时鱼类检测方法

海洋渔业过度捕捞对渔业生态安全构成严重威胁。渔业监管是维持海洋渔业生态的主要方式之一。为了提高捕鱼监管系统的智能性，提出了一种基于YOLO-V3-Tiny-MobileNet的实时鱼类检测方法。针对YOLO-V3-Tiny网络中网络层浅，特征提取能力不足的问题，以YOLO-V3-Tiny为基准，并与MobileNet相结合。拟议的网络由VOC2012数据集进行了预训练，然后在Kaggle_ NCFM（自然保护渔业监测）数据集上进行了训练和测试。实验结果表明，与其他方法相比，该方法在参数数量，平均平均精度和检测性能上具有优越的性能。