6533b81ffe1ef96bd12785dd

RESEARCH PRODUCT

Benchmark database for fine-grained image classification of benthic macroinvertebrates

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Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data. The authors would like to thank the Academy of Finland for the grants nos. 288584 , 289076 , and 289104 funding the DETECT consortium's project (Advanced Computational and Statistical Techniques for Biomonitoring and Aquatic Ecosystem Service Management). Scopus