Cataloguing of Animal Species Utilizing Convolutional Neural Networks

The growth of urban areas in contemporary times has led to significant habitat displacement in forested regions[1]. Consequently, wild animals are compelled to enter human settlements, which often disrupt their natural behaviors. Frequently, the search for food drives these animals to venture into populated areas. This situation poses a real threat to humans who may inadvertently encounter these animals when they are in a heightened state of aggression. Therefore, there is a pressing need for the identification of wild animals at the periphery of human communities adjacent to natural habitats[2]. An effective, reliable, and robust early warning system would greatly reduce the risk of deadly human- animal conflicts, thereby safeguarding both human lives and protecting endangered species.Moreover, such a system would also be useful in wildlife sanctuaries and biosphere reserves to monitor the movement of animals at the border areas of such establishments which have often proved difficult to control[4]. The usage of technology and robust cameras is not an alien concept in most major biosphere reserves and national parks around the world. Although there has been a considerable amount of progress, software-based tools have not been explored to a satisfactory extent in these use cases.[3] Computer vision has the ability to transform the tracking and monitoring process with the accuracy that its components and supporting techniques provide[5][8]. The automation-augmented reduction of man-hours invested in searching for and tracking wild animals is perhaps the biggest potential boon that computer vision can provide. The pre-processing involved in the application of computer vision algorithms is often under-documented although it plays a key role in the success of the algorithm[6]. A deep understanding of the nature of the inputs is necessary to make appropriate changes at crucial junctures of processing to meet the often- convoluted criteria required by complicated deep learning algorithms. Transforming the images is invariably necessitated due to the erratic nature of real-world data feeds[4]. The absence of an artificial synthesis element in the generation of inputs via raw camera stills adds to the intricacies involved in the image processing component. Many researchers have studied on the image classification. The purpose of the study is to establish a model that can realize the animal image classifing by using CNN. In this paper, first of all, a suitable data set is collected for the research[5]. Secondly, the parameters and layers of the neural network are chosen. Finally, a suitable criterion is established for evaluating the model so as to find the most suitable model to solve the research problem[8].