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Updated: Jan 13, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Research on Sika Deer Behavior Recognition Based on YOLOv11 Lightweight SDB-YOLO Model for Small Sample Learning.

He Gong^1,2,3, Zuoqi Wang¹, Jinghuan Hu¹

¹College of Information Technology, Jilin Agricultural University, Changchun 130118, China.

Animals : an Open Access Journal From MDPI

|January 10, 2026

Summary

This study introduces SDB-YOLO, a lightweight model for sika deer behavior recognition. It achieves high accuracy with reduced computation, improving feature representation and cross-scale modeling for small-sample datasets.

Keywords:

YOLOv11 lightweight model pose recognition sika deer behavior

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Area of Science:

Computer Vision
Animal Behavior Analysis
Machine Learning

Background:

Sika deer behavior recognition is challenging due to small sample sizes and environmental factors like illumination occlusion.
Existing models struggle with insufficient feature representation and weak cross-scale modeling.

Purpose of the Study:

To develop a lightweight and effective model for sika deer behavior recognition, particularly for small-sample scenarios.
To enhance feature fusion, fine-grained behavior modeling, and key information extraction.

Main Methods:

Proposed a novel SDB-YOLO model based on YOLOv11n, incorporating a Feature Pyramid Shared Convolution (FPSC) module for improved multi-scale feature correlation.
Introduced Ghost feature generation and dynamic convolution into the C3k2 module (C3_GDConv) for enhanced fine-grained modeling and reduced computation.
Integrated the CBAM attention mechanism and replaced the detection head with EfficientHead for better feature extraction and robust training.

Main Results:

SDB-YOLO achieved 90.2% detection accuracy.
The model operates with only 4.3 GFLOPs, demonstrating a lightweight design.
Significant performance improvements were observed compared to the baseline YOLOv11n model.

Conclusions:

The proposed SDB-YOLO model is effective and lightweight for small-sample special animal behavior recognition.
The integration of FPSC, C3_GDConv, CBAM, and EfficientHead contributes to enhanced accuracy and efficiency.