论文标题
使用深度学习来计算声纳图像中的鱼和海豚
Counting Fish and Dolphins in Sonar Images Using Deep Learning
论文作者
论文摘要
考虑到亚马逊河的鱼与海豚丰度之间的关系以及由于森林砍伐的结果，深入学习提供了改善矛盾报告的机会。当前的鱼类和海豚丰度估计方法是通过使用视觉和捕获/释放策略的现场采样来执行的。我们提出了一种新颖的方法，可以使用深度学习来计算鱼类的丰度，从而从拖船后面拍摄的声纳图像中的海豚估算值。我们考虑了143张图像的数据集，范围为0-34 FISH，以及由基金亚马逊研究小组提供的0-3个海豚。为了克服数据限制，我们测试了非常规15/85培训/测试拆分的数据增强功能。使用20次训练图像，我们使用增强的背景和从训练组中获取的随机放置/旋转的鱼类和海豚来模拟最多25,000张图像的数据梯度。然后，我们训练四个多任务网络体系结构：Densenet201，InceptionNetv2，Xception和MobilenetV2，使用两种函数近似方法预测鱼类和海豚数：回归和分类。对于回归，Densenet201对鱼类的表现最佳，对海豚的最佳X感染者的平均平方误差分别为2.11和0.133。对于分类，InceptionResnETV2对鱼类和Mobilenetv2最佳表现最佳，平均误差分别为2.07和0.245。考虑到123个测试图像，我们的结果表明，有限的声纳数据集的数据模拟成功。我们发现Densenet201能够在大约5000次训练图像后识别海豚，而鱼需要全部25,000。我们的方法可用于降低成本，并加快对全球亚马逊河和河流系统实时的鱼类和海豚丰度的数据分析。
Deep learning provides the opportunity to improve upon conflicting reports considering the relationship between the Amazon river's fish and dolphin abundance and reduced canopy cover as a result of deforestation. Current methods of fish and dolphin abundance estimates are performed by on-site sampling using visual and capture/release strategies. We propose a novel approach to calculating fish abundance using deep learning for fish and dolphin estimates from sonar images taken from the back of a trolling boat. We consider a data set of 143 images ranging from 0-34 fish, and 0-3 dolphins provided by the Fund Amazonia research group. To overcome the data limitation, we test the capabilities of data augmentation on an unconventional 15/85 training/testing split. Using 20 training images, we simulate a gradient of data up to 25,000 images using augmented backgrounds and randomly placed/rotation cropped fish and dolphin taken from the training set. We then train four multitask network architectures: DenseNet201, InceptionNetV2, Xception, and MobileNetV2 to predict fish and dolphin numbers using two function approximation methods: regression and classification. For regression, Densenet201 performed best for fish and Xception best for dolphin with mean squared errors of 2.11 and 0.133 respectively. For classification, InceptionResNetV2 performed best for fish and MobileNetV2 best for dolphins with a mean error of 2.07 and 0.245 respectively. Considering the 123 testing images, our results show the success of data simulation for limited sonar data sets. We find DenseNet201 is able to identify dolphins after approximately 5000 training images, while fish required the full 25,000. Our method can be used to lower costs and expedite the data analysis of fish and dolphin abundance to real-time along the Amazon river and river systems worldwide.
