摘要
针对猪只人工计数方法消耗时间和劳动力,育肥猪较为活跃且喜好聚集,图像中存在大量的高密度区域,导致猪只之间互相粘连、遮挡等问题,基于SOLO v2实例分割算法,提出了一种自然养殖场景下融合多尺度特征金字塔与二代可变形卷积的高密度群养猪计数模型。通过优化模型结构来减少计算资源的消耗与占用。将科大讯飞给出的猪只计数的公开数据集划分为猪只分割数据集和猪只盘点测试集,利用猪只分割数据集获得较好的分割模型,然后在猪只盘点测试集中测试盘点准确率,实现猪群分割和猪只计数。实验结果表明,本文提出的高密度猪只计数模型的分割准确率达到96.7%,且模型内存占用量为256 MB,为改进前的2/3,实现了遮挡、粘连和重叠情况下的猪只个体高准确率分割。在含有500幅猪只图像计数测试集中,模型计算猪只数量误差为0时的图像数量为207幅,较改进前提高26%。模型计算猪只数量误差小于2头猪的图像数量占测试图像总数量的97.2%。模型计算猪只数量误差大于3头猪的图像数量占总体图像数量比例仅为1%。最后,对比基于YOLO v5的群养猪计数方法,本文模型具有更优的分割效果和计数准确率,验证了本文方法对群养猪只计数的有效性。因此,本文模型既实现了高密度猪群的精准计数,还通过优化模型结构大大降低了模型对计算设备的依赖,使其适用于养殖场内猪群在线计数。
Pig counting is a critical task in large-scale breeding and intelligent management,and the manual counting method is time-consuming and labor-intensive.Since fattening pigs are more active and like to congregate,there are many high-density areas in the image,which causes problems such as adhesion and occlusion between pigs,making pig counting difficult.Based on the SOLO v2 instance segmentation algorithm,a high-density group pig counting model in natural breeding scenarios was proposed,which incorporated multi-scale feature pyramids and deformable convolutions networks version 2.Further,by optimizing the model structure,the consumption and occupation of computing resources were reduced.The pig count dataset published by iFLYTEK was divided into two parts:pig segmentation dataset and pig count test set.The pig segmentation dataset was used to train the segmentation model to achieve herd segmentation and pig counts,and the inventory accuracy was tested in the pig inventory test set.The experimental results showed that the high-density pig counting model proposed had a segmentation accuracy of 96.7%and a model weight size of 256 MB,which was 1/3 less than that before the improvement.Each improved method proposed improved the model’s segmentation accuracy and achieved high-accuracy segmentation of individual pigs in the case of occlusion,adhesion,and overlap.In the 500-image pig counting test set,the number of images when the model counted pigs with an error of 0 was 207,which was 26%more than that before the improvement.The number of images when the error of the model counting pigs was less than two pigs,accounted for 97.2%of the total number of test images.The number of images with a counting error of more than three pigs,accounted for only 1%of the total number of images.Finally,for the pig herd counting method based on YOLO v5,the model had a better segmentation effect and counting accuracy,proving the method’s effectiveness for counting pigs in groups.Therefore,the model presented not only achieved accurate
作者
王荣
高荣华
李奇峰
冯璐
白强
马为红
WANG Rong;GAO Ronghua;LI Qifeng;FENG Lu;BAI Qiang;MA Weihong(Research Center of Information Technology,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;College of Information Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China;National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第10期252-260,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
北京市自然科学基金项目(4202029)
