广东农业科学2026，Vol.53Issue(4)：74-83,10.DOI:10.16768/j.issn.1004-874X.2026.04.007
基于融合可见光与红外热成像的母猪发情识别方法研究
A Sow Estrus Detection Method Based on Feature-level Fusion of Visible Light and Infrared Thermal Imaging
摘要
Abstract
[Objective]Sow estrus recognition is essential for determining insemination timing and improving reproductive efficiency.Aiming at the problems of strong subjectivity and poor continuous monitoring capability of traditional manual estrus detection,as well as the limited robustness of single-modal methods in complex pig house environments,this study explored the effect of feature-level fusion of visible light and infrared thermal imaging on estrus recognition,and proposed a non-contact intelligent estrus detection method.[Method]Weaned multiparous Large White sows were selected as the research objects.Visible light images and infrared thermal imaging data of their vulva regions were collected twice daily continuously.Paired visible light-infrared samples were constructed based on timestamps.A two-branch structure was adopted for feature extraction.The visible light branch employed ResNet-50 with the backbone network retained to extract image spatial features,and adopted BiLSTM combined with temporal attention mechanism for time-series modeling.The infrared branch extracted statistical temperature features of the vulva and performed full connection encoding.The high-level semantic features of the two branches were linearly projected to a unified low-dimensional space for concatenated feature-level fusion.MLP was used to learn the complementary relationship between modalities,and the Sigmoid classifier at the output end generated the final recognition results.[Result]By comparing the performance of single-modal models,different fusion strategies and ablation components,the feature-level fusion model achieved an accuracy of 93.1％,a recall rate of 94.5％,and an AUC of 96.2％on the test set.Compared with the single visible light modality,the accuracy increased by 1.5 percentage;compared with the single infrared modality,the accuracy increased by 4.6 percentage and the precision increased by 8.3 percentage.In addition,its accuracy was 1.0 and 0.3 percentage points higher than that of input-level fusion and decision-level fusion respectively.In ablation experiments,with the gradual introduction of LSTM,BiLSTM and temporal attention mechanism,the accuracy increased sequentially from 89.8％to 91.4％,92.2％and 93.1％.[Conclusion]Visible light vulvar phenotypic information and infrared thermal physiological information present strong complementarity.Feature-level fusion can effectively eliminate heterogeneous differences in the raw data layer,realize cross-modal feature interaction at a high semantic level,and significantly improve the accuracy of sow estrus recognition.关键词
母猪发情识别/可见光图像/红外热成像/特征层融合/多模态/深度学习Key words
sow estrus detection/visible light image/infrared thermal imaging/feature-level fusion/multimodal/deep learning分类
农业科技引用本文复制引用
代岳峰,赵帅,尹国安..基于融合可见光与红外热成像的母猪发情识别方法研究[J].广东农业科学,2026,53(4):74-83,10.基金项目
黑龙江省省属本科高校优秀青年教师基础研究支持计划项目(YQJH2025158) （YQJH2025158）
