Abstract:
The rapid advancement of deep learning techniques has opened up new possibilities for wildlife conservation and research. In this study, we investigate the application of a scalable deep neural network for wildlife animal recognition and identification. The primary objective is to develop an efficient and accurate system capable of automatically recognizing and identifying wildlife animals from images or video footage.
We propose a deep neural network architecture based on convolutional neural networks (CNNs) that can handle the challenges associated with wildlife animal recognition, such as variations in pose, lighting conditions, and environmental backgrounds. The network architecture consists of multiple convolutional layers for feature extraction, followed by fully connected layers for classification.
To train the network, a large-scale dataset of wildlife animal images is collected and annotated with corresponding animal labels. The dataset includes a diverse range of animal species, capturing their natural habitat and behavior. We employ transfer learning techniques to leverage pre-trained models on large-scale generic image datasets, improving the network’s ability to generalize and recognize wildlife animals accurately.
To evaluate the performance of the proposed deep neural network, comprehensive experiments are conducted on benchmark wildlife animal recognition datasets. We compare our approach with existing state-of-the-art methods and evaluate the accuracy, efficiency, and scalability of the proposed model. Additionally, we analyze the network’s robustness to variations in image quality, occlusions, and background clutter.
The results demonstrate that the proposed deep neural network achieves significant improvements in wildlife animal recognition and identification compared to traditional methods. The model exhibits high accuracy and robustness, even when applied to large-scale datasets. The system’s scalability enables efficient processing of real-time or near real-time wildlife monitoring applications, contributing to conservation efforts and wildlife research.
The findings from this study have practical implications for wildlife conservation organizations, researchers, and environmental agencies. The scalable deep neural network developed in this research can facilitate automated wildlife monitoring, animal population estimation, and habitat assessment. Furthermore, the knowledge gained from this study can contribute to the development of intelligent systems for wildlife management and protection.
Keywords: Deep neural network, Wildlife animal recognition, Wildlife conservation, Convolutional neural networks, Transfer learning, Scalability, Image classification.
STUDY OF SCALABLE DEEP NEURAL NETWORK FOR WILDLIFE ANIMAL RECOGNITION AND IDENTIFICATION, GET MOREÂ Â COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
