Hybrid VGG16-Abstract Neural Network Model based Bird Detection and Classification of Images
Keywords:
Abstract Neural Network, BiRD, CUB 200, Data Augmentation, Normalization, Hybrid VGG16- ANN=Abstract
The classification and detection of bird images has become crucial for ecological monitoring and recognition in recent years. Classifying and detecting birds in images is a challenging task due to the presence of both high and low interclass variations. To overcome these challenges, a hybrid approach is proposed in this research that combines the VGG16 architecture with Abstract Neural Network (ANN). This approach aims to overcome the challenges that arise from both high and low interclass variations and improved the accuracy. Then, BiRD and CUB 200 datasets were used to classify birds using the suggested method. Data normalization is implemented to enhance data integrity and efficiency during the database design process, which involved restructuring the data to eliminate redundancy and enhance the overall consistency of the dataset. This research utilized bounding boxes to improve detection accuracy and provide detailed spatial information about birds, leading to more precise identification. Additionally, a hybrid VGG16-ANN model is developed to address the difficulty of detecting abnormal bird activity in real- time. From the results, it clearly shows that the hybrid VGG16-ANN achieved an accuracy of 98.50% in CUB200 dataset, which is comparatively higher than the existing methods such as Compound Model Scaling with Efficient Attention (CMSEA), YOLO-V5 and Hierarchical feature fusion value of 95.37%, 97.63% and 87.02% respectively.
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