CGS-BR: Construction and Benchmarking of a Respiratory Behavior Dataset for the Chinese Giant Salamander

The Chinese giant salamander (Andrias davidianus) is a nationally protected species in China, and its respiratory behavior serves as a key indicator of its physiological state, health status, and biological rhythm. However, research on intelligent monitoring of its respiratory behavior remains limited due to several challenges, including the species' nocturnal habits, resulting in low image contrast and poor quality in dark environments; extremely subtle breathing movements; and high-cost manual annotation, leading to a scarcity of high-quality annotated visual data. These factors severely constrain the application of deep learning techniques in this field. To support research on respiratory behavior monitoring in the Chinese giant salamander, this study constructs and releases the CGS-BR dataset, which is the first vision-based dataset dedicated specifically to respiratory behavior detection in this species. The dataset was collected under controlled simulated breeding conditions and consists of 1732 images extracted from 215 high-definition video clips. Following a standardized procedure, each complete respiratory cycle is manually annotated into four stages: head-up, diving, exhalation, and inhalation. To validate the effectiveness of this dataset, this study selects YOLOv8n as the baseline model, which balances detection accuracy, speed, and parameter count, enabling efficient giant salamander respiratory detection under limited resources. By comparing it with several representative models, we provide a reliable evaluation of the dataset's applicability. CGS-BR aims to provide fundamental data support for research on respiratory monitoring in the Chinese giant salamander, laying the foundation for subsequent applications in conservation management, captive breeding, health monitoring, and early disease warning.