Creating a Dataset for the Detection of Flying Birds

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. We present a pipeline for collecting a dataset for the detection of flying birds in videos. We treat the creation of a dataset as an iterative task, which allows to train an inference system periodically, and to improve gradually its performance. We follow a three stage pipeline, that includes repetitive video recording, video annotation and frame sampling for training a Deep Learning detector. Furthermore, we would like to point out that the end goal of our work is to protect birds flying through wind farms, in order to take protective measures. Therefore it is crucial to find the moment that a bird enters and exits the field of view of the camera. Thus we use our dataset to setup a Temporal Action Detection (TAD) task, and we evaluate our inference pipeline using an appropriate measure. https://doi.org/10.32942/X2S33X Artificial Intelligence and Robotics, Ornithology wildlife protection, wind farm, machine learning, dataset compilation Published: 2024-09-20 12:11 CC-By Attribution-NonCommercial-NoDerivatives 4.0 International Language: English