A deep learning approach to detect and identify live freshwater macroinvertebrates

The study of macroinvertebrates using computer vision is in its infancy and still faces multiple challenges including destructive sampling, low signal-to-noise ratios, and the complexity to choose a model algorithm among multiple existing ones. In order to deal with those challenges, we propose here a new framework, dubbed 'MacroNet,' for the monitoring, i.e., detection and identification at the morphospecies level, of live aquatic macroinvertebrates. This framework is based on an enhanced RetinaNet model. Pre-processing steps are suggested to enhance the characterization propriety of the original algorithm. The images are split into fixed-size tiles to better detect and identify small macroinvertebrates. The tiles are then fed as an input to the model, and the resulting bounding box is assembled. We have optimized the anchor boxes generation process for high detection performance using the k-medoid algorithm. In order to enhance the localization accuracy of the original RetinaNet model, the complete intersection over union loss has been integrated as a regression loss to replace the standard loss (a smooth l1 norm). Experimental results show that MacroNet outperforms the original RetinaNet model on our database and can achieve on average 74.93% average precision (AP), depending on the taxon identity. In our database, taxa were identified at various taxonomic levels, from species to order. Overall, the proposed framework offers promising results for the non-lethal and cost-efficient monitoring of live freshwater macroinvertebrates.