Philipp M. Rehsen

Mikko Impiö, Philipp M. Rehsen, Jenni Raitoharju

The amount of image datasets collected for environmental monitoring purposes has increased in the past years as computer vision assisted methods have gained interest. Computer vision applications rely on high-quality datasets, making data curation important. However, data curation is often done ad-hoc and the methods used are rarely published. We present a method for curating large-scale image datasets of invertebrates that contain multiple images of the same taxa and/or specimens and have relatively uniform background in the images. Our approach is based on extracting feature embeddings with pretrained deep neural networks, and using these embeddings to find visually most distinct images by comparing their embeddings to the group prototype embedding. Also, we show that a simple area-based size comparison approach is able to find a lot of common erroneous images, such as images containing detached body parts and misclassified samples. In addition to the method, we propose using novel metrics for evaluating human-in-the-loop outlier detection methods. The implementations of the proposed curation methods, as well as a benchmark dataset containing annotated erroneous images, are publicly available in https://github.com/mikkoim/taxonomist-studio.

Mikko Impiö, Philipp M. Rehsen, Jarrett Blair et al.

The ability to estimate invertebrate biomass using only images could help scaling up quantitative biodiversity monitoring efforts. Computer vision-based methods have the potential to omit the manual, time-consuming, and destructive process of dry weighing specimens. We present two approaches for dry mass estimation that do not require additional manual effort apart from imaging the specimens: fitting a linear model with novel predictors, automatically calculated by an imaging device, and training a family of end-to-end deep neural networks for the task, using single-view, multi-view, and metadata-aware architectures. We propose using area and sinking speed as predictors. These can be calculated with BIODISCOVER, which is a dual-camera system that captures image sequences of specimens sinking in an ethanol column. For this study, we collected a large dataset of dry mass measurement and image sequence pairs to train and evaluate models. We show that our methods can estimate specimen dry mass even with complex and visually diverse specimen morphologies. Combined with automatic taxonomic classification, our approach is an accurate method for group-level dry mass estimation, with a median percentage error of 10-20% for individuals. We highlight the importance of choosing appropriate evaluation metrics, and encourage using both percentage errors and absolute errors as metrics, because they measure different properties. We also explore different optimization losses, data augmentation methods, and model architectures for training deep-learning models.

Mikko Impiö, Philipp M. Rehsen, Tiina Laamanen et al.

This paper presents the AquaMonitor dataset, the first large computer vision dataset of aquatic invertebrates collected during routine environmental monitoring. While several large species identification datasets exist, they are rarely collected using standardized collection protocols, and none focus on aquatic invertebrates, which are particularly laborious to collect. For AquaMonitor, we imaged all specimens from two years of monitoring whenever imaging was possible given practical limitations. The dataset enables the evaluation of automated identification methods for real-life monitoring purposes using a realistically challenging and unbiased setup. The dataset has 2.7M images from 43,189 specimens, DNA sequences for 1358 specimens, and dry mass and size measurements for 1494 specimens, making it also one of the largest biological multi-view and multimodal datasets to date. We define three benchmark tasks and provide strong baselines for these: 1) Monitoring benchmark, reflecting real-life deployment challenges such as open-set recognition, distribution shift, and extreme class imbalance, 2) Classification benchmark, which follows a standard fine-grained visual categorization setup, and 3) Few-shot benchmark, which targets classes with only few training examples from very fine-grained categories. Advancements on the Monitoring benchmark can directly translate to improvement of aquatic biodiversity monitoring, which is an important component of regular legislative water quality assessment in many countries.