Rapid three-dimensional detection of harmful algae using holographic microimaging

Abstract

Holographic microimaging technology is widely used in the plankton detection due to its large depth of field and ability to record three-dimensional (3D) information. However, traditional methods involve tedious and time-consuming steps, e.g., region of interest (ROI) extraction, dense reconstruction, focused evaluation, and focused ROI recognition. As a result, traditional methods are difficult to detect in real time. In this paper, we propose a neural network-based 3D detection method, which for the first time uses the holograms to obtain 3D sampling data for algae detection, and innovatively designs a convolutional neural network (DHCNN) to improve the speed and accuracy of detection. A dataset containing 3D samples of eight harmful algae species is established, and the trained DHCNN model achieves a mean average precision of 99.2 % on the test set. We find that accuracy is higher with 3D sampled data, which contains more valid information than with autofocused ROIs. Moreover, detection rate is 99.5 %, which means it could detect attached algae and algae abundance statistics are more accurate. And detection time of the proposed method is only 0.066 s, which is 20 times faster than the traditional method. We believe that the proposed method represents an effective approach for real-time algae monitoring.