Abstract This paper proposes a new approach combining YOLOv3 with MobileNetv1 for fish detection in real breeding farm. The feature maps of MobileNet are reselected as per their receptive fields for better fish detection instead of fixed chosen strategy in the original YOLOv3 framework. A set of fish image data acquired in breeding farm is used to evaluate the proposed method. The high accuracy of detection results is achieved to confirm the effectiveness of the proposed method. Furthermore, taking the place of “ImageNet”, a slighter dataset including fish images with 16 species for backbone network pretraining is picked out from “ImageNet” to extract fish features. On this basis, the effect of detection of the model is further improved due to that the extracted features are more closed to fish objects. Therefore, the proposed method is proved to have the capability of providing necessary and accurate number of fish, which will then be used to determine the breeding actions accordingly.

中文翻译：

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基于MobileNetv1作为主干的改进型YOLOv3鱼类检测模型

摘要 本文提出了一种结合YOLOv3和MobileNetv1的真实养殖场鱼类检测新方法。MobileNet 的特征图根据它们的感受野重新选择，以更好地检测鱼，而不是原始 YOLOv3 框架中的固定选择策略。使用在养殖场获得的一组鱼类图像数据来评估所提出的方法。检测结果的高精度证明了所提出方法的有效性。此外，代替“ImageNet”，从“ImageNet”中挑选出一个包含16种鱼类图像的较小数据集用于骨干网络预训练，以提取鱼类特征。在此基础上，由于提取的特征更接近于鱼类目标，模型的检测效果进一步提升。所以，