Abstract
Many ecological studies rely on count data and involve manual counting of objects of interest, which is time-consuming and especially disadvantageous when time in the field or lab is limited. However, an increasing number of works uses digital imagery, which opens opportunities to automatise counting tasks. In this study, we use machine learning to automate counting objects of interest without the need to label individual objects. By leveraging already existing image-level annotations, this approach can also give value to historical data that were collected and annotated over longer time series (typical for many ecological studies), without the aim of deep learning applications. We demonstrate deep learning regression on two fundamentally different counting tasks: (i) daily growth rings from microscopic images of fish otolith (i.e., hearing stone) and (ii) hauled out seals from highly variable aerial imagery. In the otolith images, our deep learning-based regressor yields an RMSE of 3.40 day-rings and an R^2 of 0.92. Initial performance in the seal images is lower (RMSE of 23.46 seals and R^2 of 0.72), which can be attributed to a lack of images with a high number of seals in the initial training set, compared to the test set. We then show how to improve performance substantially (RMSE of 19.03 seals and R^2 of 0.77) by carefully selecting and relabelling just 100 additional training images based on initial model prediction discrepancy. The regression-based approach used here returns accurate counts (R^2 of 0.92 and 0.77 for the rings and seals, respectively), directly usable in ecological research.
Highlights
Most successful ML models belong to the family of Deep Learning (DL)[12], in particular Convolutional Neural Networks (CNNs)[13]
The second dataset consists of aerial images of grey seals (Halichoerus grypus) and harbour seals (Phoca vitulina) hauled out on land, which are collected from an aircraft using a hand-held camera during annual surveys monitoring population size and distribution[8]
The regression-based CNN presented here performed well when trained on the two fundamentally different datasets. This was achieved without making any modifications to the architecture of the CNN between the two cases, except for training hyperparameters like the learning rate and number of epochs
Summary
Most successful ML models belong to the family of Deep Learning (DL)[12], in particular Convolutional Neural Networks (CNNs)[13]. The second dataset consists of aerial images of grey seals (Halichoerus grypus) and harbour seals (Phoca vitulina) hauled out on land, which are collected from an aircraft using a hand-held camera during annual surveys monitoring population size and distribution[8] These images are highly variable in light conditions, distance towards the seals, focal length and angle of view. Instead of recounting the seals and correcting the annotations for all images in this dataset, we propose a multi-step model building approach to handle scenarios where the quality of existing image-level annotations is insufficient to train a CNN This approach can be used to adapt the CNN to dataset variations that appear over time or with new acquisitions conditions. They allow researchers to reassign valuable resources and scale up their surveying effort, while potentially leveraging existing image-level annotations from archived datasets directly for the automation of counting
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