Automatic Identification and Counting of South African Animal Species in Camera Traps Using Deep Learning

In the area of ecology, counting animals to estimate population size and types of species is important for the wildlife conservation. This includes analysing massive volumes of image, video or audio/acoustic data and traditional counting techniques. Automating the process of identifying, classifying and counting animals would be helpful to researchers as it will phase out the tedious human–labour tasks of manual counting and labelling. The intention of this work is to address manual identification and counting methods of images by implementing an automated solution using computer vision and deep learning. This study applies a classification model to classify species and trains an object detection model using deep convolutional neural networks to automatically identify and determine the count of four mammal species in 3304 images extracted from camera traps. The image classification model reports a classification accuracy of 98%, and the YOLOv8 object detection model automatically detects buffalo, elephant, rhino and zebra school mean average precision of 50 of 89% and mean average precision of 50–95 of 72.2% and provides an accurate count over all animal classes. Furthermore, it performs well across various image scenarios such as blurriness, day, night and images displaying multiple species compared to the RT‐DETR model. The results of the study display that the application of computer vision and deep learning methods on data‐scarce and data‐enriched scenarios, respectively, can conserve biologists and ecologists an enormous amount of time used on time‐consuming human tasks methods of analysis and counting. The high‐performing deep learning models developed capable of accurately classifying and localising multiple species can be integrated into the existing conservation workflows to process large volumes of camera trap images in real time. This integration can significantly reduce the manual labour required for labelling and counting, improve the consistency and speed of wildlife surveys and enable timely decision‐making in habitat protection, population assessment and antipoaching initiatives. Additionally, these automated identification techniques can contribute towards enhancing wildlife conservation and future studies.