Automated Auricular Surface Temperature Monitoring in Asian Elephants Using Deep Learning and Infrared Thermography

Le résumé fourni par la source

Asian elephants (Elephas maximus) face substantial thermoregulatory constraints because of their large body size, low relative surface area, sparse hair, and lack of functional sweat glands. Reliable body temperature measurement is essential for assessing thermal status and evaluating welfare in both wild and managed populations, but conventional rectal thermometry requires close physical contact, animal training, and repeated manual handling, making high-frequency, continuous, large-scale monitoring impractical. This study developed a non-invasive framework for automatically detecting the outer ear and extracting auricular surface temperature from infrared thermograms. Rectal temperature, regional surface temperatures, ambient temperature, and relative humidity were measured synchronously in eight semi-captive Asian elephants, yielding 425 matched observations. The associations between rectal temperature and the surface temperatures of three anatomical regions (head, outer ear, torso and limbs) were analyzed using repeated-measures correlation accounting for the non-independence of repeated measurements. Mean outer-ear temperature showed the strongest within-individual association with rectal temperature (rrm = 0.395, p < 0.001), identifying the outer ear as the optimal thermal window for subsequent automated monitoring. Eight lightweight YOLO models—YOLOv5n, YOLOv5s, YOLOv8n, YOLOv8s, YOLO11n, YOLO11s, YOLO26n, and YOLO26s—were trained on 2178 annotated infrared images and evaluated on an independent 194-image test set from extra elephants. Model performance was assessed using detection metrics, inference speed, Bland–Altman agreement, Taylor diagram statistics, and a weighted multi-criteria score with Monte Carlo sensitivity analysis. YOLO11n achieved the best overall performance, with an mAP50 of 0.933 and an inference speed of 164 frames per second. The proposed framework provides an efficient method for automated auricular temperature monitoring and has potential applications in elephant welfare management and remote physiological surveillance.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.