The traditional monitoring of forest musk deer behavior primarily relies on direct human observation or the post hoc playback analysis of ordinary surveillance videos. This approach is not only time-consuming and labor-intensive but also highly subjective, easily leading to missing or misjudged critical behavioral information. Moreover, it is difficult to achieve real-time monitoring and anomaly warning. These limitations severely constrain the efficiency of the large-scale artificial breeding of forest musk deer and the effective advancement of wild population conservation. Thus, this study proposes a forest musk deer behavioral recognition method based on an improved YOLOv8-Pose. A forest musk deer behavior image dataset covering four typical behaviors was constructed, and 18 keypoints were systematically annotated. This study designs a Dilated Spatial Pyramid Pooling-Fast (DILATED-SPPF) module and a Multi-scale Depthwise Separable Context Mixer (MDSC-Mixer) module, and integrates them into YOLOv8-Pose. Experimental results show that the improved model outperforms the original YOLOv8-Pose and comparison models such as YOLOv11/v12-Pose on key metrics of object detection (Box-mAP50 0.929, Box-mAP50-95 0.814) and pose estimation (Pose-mAP50 0.879, Pose-mAP50-95 0.565). This study further develops a visual interactive interface that intuitively presents detection results and skeleton structures. This work provides a high-precision, low-cost automated behavior analysis tool for the artificial breeding and wild conservation of forest musk deer with significant application value for enhancing the intelligence level of endangered species protection.

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.

Non-contact behavioral monitoring is important for low-disturbance husbandry of captive forest musk deer, but variable illumination, shadows, partial occlusion, and low target-background contrast can reduce recognition reliability. This study developed HGS-YOLO26n, a lightweight real-time detector for continuous behavior recording under practical captive breeding conditions. HVIEnhanceStem was used to preserve shallow visual cues under weak illumination and low contrast, AdaGFB adapted feature extraction to posture-related shifts in local discriminative regions, and SDIoU improved bounding-box localization under occlusion and target-scale variation. The model was trained and evaluated using 6760 field images collected from 30 enclosures. HGS-YOLO26n achieved an mAP50–95 of 0.8409, exceeding the YOLO26n baseline by 3.74 percentage points, with 2.84 M parameters and an inference speed of 176.8 frames per second. Validation on continuous videos showed that its behavior-duration and event-count estimates were closer overall to blinded manual observations than those of the comparison models. By converting routine surveillance footage into longitudinal, individual-level records of behavior duration, event frequency, and temporal distribution, the system enables efficient review of behavioral activity across extended monitoring periods. Husbandry and veterinary staff can use these records to compare behavioral patterns across observation periods and return to corresponding video segments when specific changes require verification. This provides a low-disturbance source of quantitative behavioral information for routine husbandry, longitudinal observation, and conservation breeding of captive forest musk deer.