A Lightweight Real-Time Vision System for Low-Disturbance Veterinary Management and Behavioral Welfare Assessment in Captive Forest Musk Deer

Non-contact behavioral monitoring is essential for the low-disturbance husbandry of captive forest musk deer, yet environmental factors such as variable illumination, shadows, and occlusion often hinder automatic recognition accuracy. To address these challenges, this study develops and evaluates a lightweight real-time detector, called HGS-YOLO26n, for behavior recording under practical captive breeding conditions. The model, which integrates visibility adaptation and geometric localization optimiza-tion, is trained on 6,760 field images from 30 enclosures. Results indicate that HGS-YOLO26n achieves an mAP50-95 of 0.8409, outperforming the baseline YO-LO26n by 3.74% while maintaining high efficiency with 2.84M parameters and an in-ference speed of 176.8 frames per second. Validation on continuous video segments demonstrate that the behavioral metrics of the system aligned closely with blinded manual assessments, yielding a consistency score of 96.0 compared to 89.9 for the baseline model. Consequently, this technology effectively converts ordinary surveil-lance footage into quantitative behavioral datasets. These experiments suggest that the proposed HGS-YOLO26n offers a robust, simple and efficient solution for wildlife daily management and welfare monitoring, facilitating standardized veterinary evaluation without imposing additional stress on captive forest musk deer.