Non-Invasive Individual Re-Identification of Water Monitors (Varanus salvator) Using Deep Learning

Abstract

Effective management of urban Asian water monitor (Varanus salvator (Laurenti, 1768)) populations requires precise individual identification, yet traditional physical-marking methods remain invasive and labor-intensive. This study developed a non-invasive, automated photographic re-identification (Re-ID) system using deep learning and computer vision to facilitate population monitoring in semi-urban environments. We evaluated seven deep learning configurations based on ResNet50 incorporating Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), and Batch Normalization neck (BNNeck) optimizations and benchmarked them against traditional feature matching (HotSpotter) using an open-set evaluation dataset of 3311 images across 161 Side-IDs focusing on unique lateral head-scale patterns. HotSpotter demonstrated immediate field viability, achieving a Rank-1 accuracy of 99.88% and a mean Average Precision (mAP) of 90.40%. Among the deep learning architectures, the baseline ResNet50 achieved the highest Rank-1 accuracy of 75.39% and mAP of 55.97%. As a decision-support framework, the deep learning pipeline achieved over 87% Rank-5 accuracy, drastically reducing manual screening effort and cognitive load during capture–mark–recapture surveys. This non-invasive framework establishes a scalable, welfare-friendly protocol for long-term urban wildlife management and biodiversity monitoring.