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Mamba-YOLO-SRC: An Automatic Deep Learning Framework for Respiratory Behavior Detection in the Chinese Giant

Dingwei Mao¹, Yan Zhou^2,3, Chenyang Shi¹

¹School of Computer Science and Engineering, Jishou University, Jishou 416000, China.

Animals : an Open Access Journal From MDPI

|June 26, 2026

Summary

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Neural Control of Respiration

Neural Control of Respiration

The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...

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

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Correction: Li et al. Improved Chinese Giant Salamander Parental Care Behavior Detection Based on YOLOv8. <i>Animals</i> 2024, <i>14</i>, 2089.

Animals : an open access journal from MDPI·2026

This study introduces Mamba-YOLO-SRC, an automated system for monitoring Chinese giant salamander (Andrias davidianus) respiration. This novel method accurately detects key behaviors, aiding conservation efforts for this endangered species.

Area of Science:

Amphibian biology
Conservation science
Bio-inspired computing

Background:

Chinese giant salamanders (Andrias davidianus) exhibit abnormal respiratory behaviors indicating health decline.
Traditional monitoring is difficult due to their nocturnal, cave-dwelling habits and limitations in manual observation.
Accurate assessment of pulmonary respiration is vital for captive breeding and conservation of this endangered species.

Purpose of the Study:

To develop the first automated method for monitoring respiratory behaviors in Chinese giant salamanders.
To address the challenges posed by the species' elusive nature and limitations of conventional detection methods.
To provide a reliable tool for advancing research on salamander health and behavior.

Main Methods:

Keywords:

Andrias davidianus YOLO deep learning hybrid detection model respiratory behavior

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Proposed Mamba-YOLO-SRC, a hybrid detection framework combining Mamba and YOLO architectures.

Developed a system to accurately identify four key respiratory behaviors: diving (Dive), head-raising (HeadUP), inhalation (Inhale), and exhalation (Exhale).

Validated the model's performance using established metrics.

Main Results:

Achieved a high mean average precision (mAP@0.5) of 0.944.
Demonstrated strong per-class performance: Dive (0.975), HeadUP (0.925), Exhale (0.948), and Inhale (0.928).
The Mamba-YOLO-SRC framework proved effective in identifying specific salamander respiratory actions.

Conclusions:

Mamba-YOLO-SRC offers a feasible and referable technical solution for automated respiratory behavior monitoring.
This method significantly advances research capabilities for Chinese giant salamanders in both captive and natural environments.
The system supports improved health assessment and conservation strategies for Andrias davidianus.