A deep-learning based approach to detect and classify animals flying near wind turbines using thermal surveillance cameras and open-source software
Yarbrough, J.; Cuntiz, I.; Schipper, J.; Lawson, M.; Straw, B.; Hein, C.; Cryan, P.
Show abstract
The authors have withdrawn their manuscript because it was distributed without the proper approvals from the United States Geological Survey. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Pre-training artificial neural networks with spontaneous retinal activity improves motion prediction in natural scenes 93%
- Automatic wound detection and size estimation using deep learning algorithms 92%
- The benefits of insect-swarm hunting in echolocating bats, and its influence on the evolution of bat echolocation signals 92%
Similar papers in this journal
- An Assistive Computer Vision Tool to Automatically Detect Changes in Fish Behavior In Response to Ambient Odor 95%
- A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images 94%
- Embracing firefly flash pattern variability with data-driven species classification 93%
Similar papers in this journal
- tUbe net: a generalisable deep learning tool for 3D vessel segmentation 91%
- Convolutional-LSTM Approach for Temporal Catch Hotspots (CATCH): An AI-Driven Model for Spatiotemporal Forecasting of Fisheries Catch Probability Densities 91%
- refineDLC: an advanced post-processing pipeline for DeepLabCut outputs 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.