Deep learning–based automatic bird identification system for offshore wind farms
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||14|
|Publication status||Published - 2020|
|Publication type||A1 Journal article-refereed|
In the proposed system, a separate radar system detects birds and provides WGS84 coordinates to a steering system of a camera. The steering system consists of a motorized video head and our software to control it. The steering system tracks flying birds in order to capture series of images by a digital single-lens reflex camera. Classification is based on these images, and it is implemented by convolutional neural network trained with a deep learning algorithm. We applied to the images our data augmentation method, in which images are rotated and converted into different color temperatures. The results indicate that the proposed system has good performance to identify bird species in the test area. Aiming accuracy for the video head was 88.91 %. Image classification performance as true positive rate was 0.8688.
- machine learning, deep learning, convolutional neural networks, image classification, intelligent surveillance systems, wind farms