Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay

In this paper, we propose an instrumentation andcomputer vision pipeline that allows automatic object detectionon images taken from multiple experimental set ups. We demon-strate the approach by autonomously counting intoxicated fliesin the FLORIDA assay. The assay measures the effect of ethanolexposure onto the ability of a vinegar fly Drosophila melanogasterto right itself. The analysis consists of a three-step approach.First, obtaining an image of a large set of individual experiments,second, identify areas containing a single experiment, and third,discover the searched objects within the experiment. For theanalysis we facilitate well-known computer vision and machinelearning algorithms—namely color segmentation, threshold imag-ing and DBSCAN. The automation of the experiment enables anunprecedented reproducibility and consistency, while significantlydecreasing the manual labor