南京农业大学学报2025，Vol.48Issue(2)：464-475,12.DOI:10.7685/jnau.202401029
基于改进YOLOv7和图像融合的哺乳期死亡仔猪检测方法
The detection method of lactating dead pigs based on improved YOLOv7 and image fusion
摘要
Abstract
[Objectives]A method for automated detection of lactating dead pigs based on image registration fusion and improved YOLOv7 was proposed to address the issues of time-consuming and labor-intensive manual inspection of lactating dead pigs,which can easily cause stress reactions in sows.[Methods]The KAZE feature point matching algorithm was used to match visible light images with thermal infrared images.The registered images spatially using geometric transformations was aligned,and the registered images were converted,fused,and inverted into fused images using the Curvelet transform in the frequency domain.Based on the YOLOv7 model,the SE attention module was introduced and embedded in the Backbone part of the original network to form an improved model YOLOv7-SE,which reduced the interference of low dark background information in the image on target recognition and improved the detection performance of the model.[Results]The experimental results showed that the accuracy,recall,and average accuracy of the model on fused images were higher than those on visible light and thermal infrared images.Compared with the original YOLOv7,YOLOv7-SE had improved accuracy and recall by 3.2%and 4.3%respectively,with an average single image detection time of only 6.8 ms.[Conclusions]This model can achieve accurate and rapid detection of dead piglets during lactation in breeding farm scenarios.关键词
死亡仔猪/哺乳期/图像配准融合/YOLOv7-SEKey words
dead pigs/lactation/image registration fusion/YOLOv7-SE分类
信息技术与安全科学引用本文复制引用
衡熙,沈明霞,刘龙申,姚文,李鹏..基于改进YOLOv7和图像融合的哺乳期死亡仔猪检测方法[J].南京农业大学学报,2025,48(2):464-475,12.基金项目
科技创新2030——"新一代人工智能"重大项目(2021ZD0113803) （2021ZD0113803）
江苏省现代农机装备与技术示范推广项目(NJ2021-39) （NJ2021-39）
