Scrutiny of Methods for Image Detection and Recognition of Different Species of Animals

Abstract

Animal detection-based study is useful in many real-life applications. Techniques involved in animal detection are useful in observing the locomotive behavior of the engaged animal and in result it prevent harmful interruption of animals in residential areas. There are some branches of research in animal detection. Some of these branches will therefore be discussed in this journal. Humans have developed many algorithms and techniques to gain a better understanding of animal behaviour. Therefore, for early preventive measures, these technologies can also serve as a warning system for humans from encroachment of dangerous wild animals. Such tasks can be reduced to three main branches, namely animal detection, tracking and recognition. Through these papers, new approaches for study and a variety of technologies/algorithms implemented in the past are identified and appropriate ways for solving the research gaps are suggested to fill the gap.

Museum Relic Image Detection and Recognition Based on Deep Learning

“…Elham Mohammed Thabit A and others reviewed the detection and recognition methods of animal images, including the widely used algorithms, such as SVM, BP, CNN, and RNN. It can be seen that a more macro understanding was provided for image detection and recognition [ 3 ]. Loris Nanni et al adopted biological heuristic algorithm to preprocess the image.…”