Deep Learning

Object Detection and Semantic Segmentation for the Analysis of Complex Biointerfaces

We promote the utilization of modern deep learning methods for the automated analysis of complex surfaces from (short-term) field tests. These are valuable tools for the quantitative and unbiased evaluation of novel coatings. For the reliable detection of microfoulers in the early stages of biofouling, we established a fast neural network counting diatoms in difficult environments with a human-like precision on fluorescence microscopy images[1]. For the detailed determination of the sophisticated macrofouler distribution in later biofouling stages, we employ advanced semantic segmentation of common but highly diverse field panel photos with unique precision.

Students working on this project: Lutz M. Krause

[1] Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure, Lutz M. K. Krause, Julian Koc, Bodo Rosenhahn, and Axel Rosenhahn, Environmental Science & Technology 2020 54 (16), 10022-10030, DOI: 10.1021/acs.est.0c01982