BSI-EUNet: Birds Species Identification using Enhanced UNET Architecture
Keywords:
Bird species, CNN, Deep Learning, Hybrid Neural Network, E-UNET architectureAbstract
The precise and automated identification of bird species is becoming increasingly important as biodiversity assessment and conservation activities become more prominent. This paper introduces a new strategy, BSI-EUNet, for the identification of bird species in image processing and classification, in response to the increasing demand for more effective and efficient methods. The dataset containing bird species has been obtained from the Kaggle repository. The image dataset has undergone denoising using non-adaptive thresholding to improve the quality of the input images. The segmentation was performed utilizing the upgraded UNET architecture, often known as E-UNET. This form of UNET is specifically designed to enhance the accuracy and precision of segmenting bird species in photographs, resulting in a meticulous and precise depiction of the borders of each species. The training and classification process involves utilising a Hybrid Neural Network (HNN) that combines the Visual Geometry Group (VGG)-16 and Convolutional Neural Network (CNN) with a modified Densenet. This approach aims to develop a strong and adaptable model for identifying different species. The integration of these structures improves the network's capacity to detect complex patterns and hierarchical characteristics, facilitating precise categorization of various bird species. The model's higher performance is demonstrated by experimental assessments on the datasets, surpassing existing approaches.
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