Abstract
The monitoring of harmful algae is very important for the maintenance of the aquatic ecological environment. Traditional algae monitoring methods require professionals with substantial experience in algae species, which are time-consuming, expensive and limited in practice. The automatic classification of algae cell images and the identification of harmful algae images were realized by the combination of multiple Convolutional Neural Networks (CNNs) and deep learning techniques based on transfer learning in this work. 11 common harmful and 31 harmless algae genera were collected as input samples, the five CNNs classification models of AlexNet, VGG16, GoogLeNet, ResNet50, and MobileNetV2 were fine-tuned to automatically classify algae images, and the average accuracy was improved 11.9% when compared to models without fine-tuning. In order to monitor harmful algae which can cause red tides or produce toxins severely polluting drinking water, a new identification method of harmful algae which combines the recognition results of five CNN models was proposed, and the recall rate reached 98.0%. The experimental results validate that the recognition performance of harmful algae could be significantly improved by transfer learning, and the proposed identification method is effective in the preliminary screening of harmful algae and greatly reduces the workload of professional personnel.
A Crack Identification Method for Concrete Structures Using Improved U-Net Convolutional Neural Networks