Convolutional Neural Network Based Automatic Bird Identification and Monitoring System for Offshore Wind Farms

• Main Author: Niemi, Juha
• Other Authors: Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences, Tampere University
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Tampere University 2020
• Subjects: Tieto- ja sähkötekniikan tohtoriohjelma - Doctoral Programme in Computing and Electrical Engineering; wind farms; machine learning; neural networks; Lesser black-backed gull
• Online Access: https://trepo.tuni.fi/handle/10024/123975

Collisions between birds and wind turbines can be a significant problem in wind farms. Practical deterrent methods are required to prevent these collisions. However, it is improbable that a single deterrent method would work for all bird species in a given area. An automatic bird identification system is needed in order to develop bird species level deterrent methods. This thesis describes the first and necessary part of the entirety that is eventually able to monitor bird movements, identify bird species, and launch deterrent measures. The objective of this thesis is twofold: it has to detect and classify the two key bird species, and secondarily to classify maximum number of other bird species while the first part still stands. The system consists of a radar for detection of the birds, a digital single-lens reflex camera with a telephoto lens for capturing images, a motorized video head for steering the camera, and a convolutional neural network model trained on the images using a deep learning algorithm for image classification. Imbalanced data are utilized because the distribution of the captured images is naturally imbalanced. Distribution of the training data set is applied to estimate the actual distribution of the bird species in the test area. Several architectures were tested on species identification and the best results were obtained by the image classifier that is a hybrid of hierarchical and cascade models. The main idea is to train classifiers on bird species groups, in which the species resemble more each other than any other species outside the group in terms of morphology (colouration and shape). The results of this study show that the developed image classifier model has sufficient performance to identify bird species in the test area in the offshore environment. When the hybrid hierarchical model was applied to the imbalanced data sets, the proposed system classified all of the white-tailed eagles correctly (TPR= 1.0000), and the lesser black-backed gull achieved a classification performance ...