Netting Damage Detection for Marine Aquaculture Facilities Based on Improved Mask R-CNN
Netting Damage Detection for Marine Aquaculture Facilities Based on Improved Mask R-CNN
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DOI：
10.3390/jmse10070996
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发表时间：
2022-07
影响因子：
2.9
通讯作者：
Ziliang Zhang;Fukun Gui;X. Qu;Dejun Feng
中科院分区：
文献类型：
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作者：
Ziliang Zhang;Fukun Gui;X. Qu;Dejun Feng
网捕损害限制了海水养殖业的安全发展。为了准确识别和定位受损网，我们提出了一种使用改进的Mask R-CNN的检测方法。我们创建了一个图像数据集的不同类型的损害，从混合的条件，并加强它的数据增强。然后，我们将递归特征金字塔（RFP）和可变形卷积网络（DCN）结构引入到学习框架中，以优化海洋环境的基本骨干，并利用网络的高级语义和低级本地化信息构建特征映射。这种改进解决了在小的和不规则的损伤网络中检测性能差的问题。实验结果表明，这些变化显著提高了模型的平均精度，达到94.48%，比原方法提高了7.86%。增强模型执行速度快，丢失率约为7.12%，检测周期为4.74帧/秒。与传统的图像处理方法相比，该模型具有较好的鲁棒性，能够更好地平衡检测精度和速度。该方法为海水养殖环境中的渔网损伤检测提供了一种有效的解决方案。
Netting damage limits the safe development of marine aquaculture. In order to identify and locate damaged netting accurately, we propose a detection method using an improved Mask R-CNN. We create an image dataset of different kinds of damage from a mix of conditions and enhance it by data augmentation. We then introduce the Recursive Feature Pyramid (RFP) and Deformable Convolution Network (DCN) structures into the learning framework to optimize the basic backbone for a marine environment and build a feature map with both high-level semantic and low-level localization information of the network. This modification solves the problem of poor detection performance in damaged nets with small and irregular damage. Experimental results show that these changes improve the average precision of the model significantly, to 94.48%, which is 7.86% higher than the original method. The enhanced model performs rapidly, with a missing rate of about 7.12% and a detection period of 4.74 frames per second. Compared with traditional image processing methods, the proposed netting damage detection model is robust and better balances detection precision and speed. Our method provides an effective solution for detecting netting damage in marine aquaculture environments.