Zone-Wise Wildlife Detection and Population Analysis Using Computer Vision

Abstract

Wildlife monitoring is essential for biodiversity conservation, ecological research, habitat management, and protection of endangered species. Traditional wildlife population monitoring methods often require extensive fieldwork, manual observation, and expert analysis, making them time-consuming and difficult to scale across large geographical regions. The development of computer vision, machine learning, and deep learning provides an opportunity to automate wildlife detection and population analysis using camera-trap images and videos. This paper presents a review of computer-vision-based approaches for zone-wise wildlife detection and population analysis. The proposed concept divides a wildlife reserve, forest, or protected area into geographical zones and uses camera traps or other imaging devices to collect data from each zone. Deep-learning models such as YOLO, Faster R-CNN, CNNs, ResNet, EfficientNet, Vision Transformers, and related object-detection techniques can be used to identify animal species and count detected individuals. The resulting information can be combined with the location of each camera to produce zone-wise species distributions, abundance estimates, and population trends. Recent research demonstratesthat automated wildlife detection can substantially reduce the manual effort required to process large camera-trap datasets. However, challenges remain in handling small and partially visible animals, different lighting conditions, dense vegetation, similar-looking species, class imbalance, changing habitats, and limited training data. Population estimation also requires consideration of imperfect detection and repeated observations. Future systems can combine computer vision, geographic information systems, edge AI, IoT sensors, and explainable artificial intelligence to provide real-time and zone-specific wildlife monitoring.