SP SEG(Animal Image Identification and Classification)
Dakshata Chaudhari, Kanishka Labhane, Komal Maraskhole, Purva Choudhari, Prof. Madhavi Sadu
Image processing, Image classification, Image Identification, Animal Detection, Convolution neural network(CNN), VGG16.
This research paper proposes a novel method that uses TensorFlow, OpenCV, PIL, Matplotlib, VGG-16 architecture Convolutional Neural Networks (CNNs) for the classification and identification of wild animals in their natural habitats. The study advances the field of automated animal classification and identification by demonstrating the value of deep learning methods for conservation and wildlife monitoring[1,3]. The user interface will be improved, accuracy will be increased, errors will be decreased, and training and testing times will be optimized in future work[1]. Classification performance can be further improved by adjusting neural network weights and learning rates, as well as by incorporating low-level variables like shape and spatial location[1]. The research adds to the expanding body of work in the field of automated animal identification in wildlife monitoring and conservation initiatives utilizing deep learning techniques[2,1]. To protect wildlife for future generations, the suggested solution offers an effective and precise way of identifying and detecting animal species[2]. Applications for the research can be found in many fields, including crop protection, animal tracking, and wildlife conservation[2,3]. In this research paper, we have studied how to classify and identify different wildlife species and animals in the images by using Convolutional Neural networks (CNNs) and VGG16 architecture.
Article Details
Unique Paper ID: 164596

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 1646 - 1651
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