automate counting
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2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeroen P. A. Hoekendijk ◽  
Benjamin Kellenberger ◽  
Geert Aarts ◽  
Sophie Brasseur ◽  
Suzanne S. H. Poiesz ◽  
...  

AbstractMany 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 $$R^2$$ R 2 of 0.92. Initial performance in the seal images is lower (RMSE of 23.46 seals and $$R^2$$ 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 $$R^2$$ 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 ($$R^2$$ R 2 of 0.92 and 0.77 for the rings and seals, respectively), directly usable in ecological research.


2020 ◽  
Author(s):  
Victoria Wardell ◽  
Christian Esposito ◽  
Christopher R Madan ◽  
Daniela Palombo

Autobiographical memory studies conducted with narrative methods are onerous, requiring significant resources in time and labour. We have created a semi-automated process that allows autobiographical transcribing and scoring methods to be streamlined. Our paper focuses on the Autobiographical Interview (AI; Levine et al., 2002) but this method can be adapted for other narrative protocols. Specifically, here we lay out a procedure that guides researchers through the four main phases of the autobiographical narrative pipeline: (1) data collection, (2) transcribing, (3) scoring, and (4) analysis. First, we provide recommendations for incorporating transcription software to augment human transcribing. We then introduce an electronic scoring procedure for tagging narratives for scoring that incorporates the traditional AI scoring method with basic keyboard shortcuts in Microsoft Word. Finally, we provide a Python script that can be used to automate counting scored transcripts. This method accelerates the time it takes to conduct a narrative study and reduces opportunity for error in narrative quantification. Available open access on GitHub (https://github.com/cMadan/scoreAI), our pipeline makes narrative methods more accessible for future research.


2019 ◽  
Author(s):  
Ricardo Michels ◽  
Rutger Vos

AbstractMeadow birds are a group of species native to the Netherlands characterized by breeding in meadows that has been in decline over the last several decades, despite widespread conservation efforts. Agricultural intensification is thought to be one of the main causes of this decline, but no yearly data exists on the surrounding ecology of these birds. Recent efforts have tried to assess the food supply of meadow birds by setting sticky traps and counting the number of insects caught on them. However, this approach cannot be applied on a large scale since counting the insects is very labour intensive and unappealing to the volunteers that contribute to this research. To get a better assessment of the food supply at a larger scale, we present a system to automate counting of insects on sticky traps. The system is intended to process uploaded images and metadata using computer vision techniques to determine the number of insects found in photographs taken from the sticky traps.


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