基于改进YOLOv7和图像融合的哺乳期死亡仔猪检测方法OA北大核心

[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.