Computer Vision and Deep Learning Applications for Automated Wildlife Detection, Classification, and Population Monitoring

Abstract

  Automated wildlife monitoring is increasingly becoming an integral part of biodiversity conservation, ecological studies and protected area management. Continuous monitoring is challenging over large habitats, owing to the environmental conditions in which conventional field surveys can be undertaken and are labour intensive and time consuming. In recent years, intelligent systems have emerged, allowing the detection, classification, tracking and monitoring of wildlife with high accuracy from camera traps, unmanned aerial vehicles and surveillance videos thanks to the use of advanced computer vision and deep learning algorithms. In this paper, an interwoven framework for intelligent wildlife monitoring is proposed, covering the entire process from image acquisition, image pre-processing, detection, classification of animals and monitoring of their populations. All the data cleaning, image enhancement, standard wildlife datasets, optimized model training, and thorough evaluation with standard performance metrics are included in the framework. Experimental analysis for automated wildlife monitoring using convolutional neural network, transformer-based, Hybrid deep learning model. Comparative results show that hybrid architectures can always deliver more accurate detection, accurate species discrimination and accurate tracking performance in complex environments, such as occlusion, illumination changes, and complex background. In addition, the paper reviews some current research trends, such as few-shot learning, self-supervised representation learning for wildlife monitoring, explainable AI (XAI), and federated learning for privacy-preserving wildlife monitoring. The proposed framework offers a robust base for an appropriately scaled, accurate, and intelligent biodiversity assessment that will assist with the conservation planning and ecological decision making that can be used for sustainable management of wildlife in various ecosystems.