Machine learning - neural network-based algorithm
This method was applied using the Fiji platform, the DenoiSeg tool from
the CSBDeep plug-in, and the neural network algorithm for instance
segmentation (Buchholz et al.
2020). The machine learning process requires an appropriate graphics
card (e.g. NVIDIA) and the installation of several drivers and software
that operate in the background of the main software (CUDA Toolkit, GPU
support, TensorFlow, cuDNN SDK, Phyton etc.); they differ depending on
the computer’s firmware and operating system. After preparing the
computer we need to manually prepare labelling images in the graphics
software - photos enabling objects be sufficiently visible against the
background. Then we need to pair them with the raw photos. To learn this
neural network, a small number (2 - 10) of training data are needed. The
most time-consuming aspects are the image preparation (training data)
and the neural network learning process. Depending on the power of the
computer, the latter may take a few, twelve or even more hours. This
process produces a model that can be used for prediction (Buchholz et
al. 2020; Schroeder et al. 2020). After running the model in Fiji
software, cleaning the photo should take a few seconds, and then it
should take only a few more seconds to count the birds using the Analyze
Particles command in Fiji (Sandhya et al. 2011). The step-by-step
procedure is described on the website devoted to this method, from which
one can download sample data and perform the machine learning process on
one’s own computer: https://imagej.net/DenoiSeg