Detection of Wildlife Animals using Deep Learning Approaches: A Systematic Review

The detection of animals, in particular wildlife animals, is important to monitor their distribution for the conservation of animal life and to address a broad range of questions of animal researchers, such as in the study of ecosystem function and behavioural ecology, analyse the growth and development of animals, understand population dynamics, and discover the factors influencing animal movements. Researchers use camera traps that are activated when an animal enters their field, allowing them to collect millions of images of animals without disturbing them. Machine learning methods, particularly convolutional networks, have quickly risen to prominence as the preferred way for detecting and recognizing animals in camera trap images. This paper examines the major deep learning ideas relevant to the detection and recognition of wildlife animals, as well as the contributions to the field, the majority of which have been published recently. We survey the use of deep learning techniques for automated animal recognition, segmentation, and detection and provide a concise analysis and comparison of these approaches. The open challenges and prospective research directions are discussed.