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Deep but lightweight neural networks for fish detection

机译:深但轻量级的鱼类检测神经网络

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The explosive growth of the underwater images make the demand for automatic accurate object detection more and more urgent. In this paper, we introduced a deep but lightweight neural network to detect fishes. It achieved the state-of-the-art accuracy for fish detection on the dataset of ImageCLEF, which includes 24,277 fish images belonging to 12 classes. Compared with the common used detection network, such as Faster R-CNN, we change the structure of convolution layers by using some building blocks including concatenated ReLU, Inception, and HyperNet. The final network obtained best results of 89.95% mAP(mean average precision), 7.25% higher than the Faster R-CNN network on the same dataset.
机译:水下图像的爆炸性增长使得自动准确对象检测的需求越来越紧迫。在本文中,我们引入了一个深入但轻质的神经网络来检测鱼类。它达到了ImageClef数据集的鱼类检测的最先进的准确性,其中包括24,277个属于12级的鱼类图像。与常见的使用检测网络相比,例如更快的R-CNN,我们通过使用一些构建块来改变卷积层的结构,包括连接Relu,Incevion和HyperNet。最终网络获得了89.95 %MAP(平均值精度)的最佳结果,比同一数据集上的更快的R-CNN网络高7.25 %。

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