基于改进YOLO v8n-seg的羊只实例分割方法

Sheep Instance Segmentation Method Based on Improved YOLO v8n-seg

羊只实例分割是实现羊只识别和跟踪、行为分析和管理、疾病监测等任务的重要前提。针对规模化羊场复杂养殖环境中,羊只个体存在遮挡、光线昏暗、个体颜色与背景相似等情况所导致的羊只实例错检、漏检问题,提出了一种基于改进YOLO v8n-seg的羊只实例分割方法。以YOLO v8n-seg网络作为基础模型进行羊只个体分割任务,首先,引入Large separable kernel attention模块以增强模型对实例重要特征信息的捕捉能力,提高特征的代表性及模型的鲁棒性;其次,采用超实时语义分割模型DWR-Seg中的Dilation-wise residual模块替换C2f中的Bottleneck模块,以优化模型对网络高层特征的提取能力,扩展模型感受野,增强上下文语义之间的联系,生成带有丰富特征信息的新特征图;最后,引用Dilated reparam block模块对C2f进行二次改进,多次融合从网络高层提取到的特征信息,增强模型对特征的理解能力。试验结果表明,改进后的YOLO v8n-LDD-seg对羊只实例的平均分割精度mAP_(50)达到92.08%,mAP_(50:90)达到66.54%,相较于YOLO v8n-seg,分别提升3.06、3.96个百分点。YOLO v8n-LDD-seg有效提高了羊只个体检测精度,提升了羊只实例分割效果,为复杂养殖环境下羊只实例检测和分割提供了技术支持。

Sheep instance segmentation is an important prerequisite for sheep identification and tracking,behavior analysis and management,and disease monitoring.Aiming at the problem of false detection and missed detection of sheep instance detection caused by the occlusion of sheep individuals,dim light,and the similarity of individual color and background in the complex breeding environment of large-scale sheep farms,a sheep instance segmentation method based on improved YOLO v8n-seg was proposed.The YOLO v8n-seg network was used as the basic model for the individual sheep segmentation task.Firstly,the large separable kernel attention module was introduced to enhance the ability of the model to capture important feature information of the instance,which improved the representativeness of the features and the robustness of the model.Secondly,the bottleneck module in C2f was replaced by the expansion-wise residual module in DWR-Seg,a hyperreal-time semantic segmentation model,to optimize the ability of the model to extract high-level network features,expanding the receptive field of the model,and enhanced the relationship between context semantics.Generate new feature maps with rich feature information.Finally,the dilated reparam block module was used to further improve C2f,and the feature information extracted from the high level of the network was fused several times to enhance the understanding ability of the model.The experimental results showed that the average segmentation accuracy of the improved YOLO v8n-LDD-seg for sheep cases reached 92.08%at mAP_(50)and 66.54%at mAP_(50:90).Compared with YOLO v8n-seg,mAP_(50)and mAP_(50:95)were improved by 3.06 percentage points and 3.96 percentage points,respectively.YOLO v8n-LDD-seg effectively improved the detection accuracy of individual sheep,improved the segmentation effect of sheep instances,and provided technical support for the detection and segmentation of sheep instances in complex breeding environments.