The statistics of the number of rapeseed seedlings are very important for breeders and planters to conduct seed quality testing,field crop management and yield estimation.Calculating the number of seedlings is ineffic...The statistics of the number of rapeseed seedlings are very important for breeders and planters to conduct seed quality testing,field crop management and yield estimation.Calculating the number of seedlings is inefficient and cumbersome in the traditional method.In this study,a method was proposed for efficient detection and calculation of rapeseed seedling number based on improved you only look once version 5(YOLOv5)to identify objects and deep-sort to perform object tracking for rapeseed seedling video.Coordinated attention(CA)mechanism was added to the trunk of the improved YOLOv5s,which made the model more effective in identifying shaded,dense and small rapeseed seedlings.Also,the use of the GSConv module replaced the standard convolution at the neck,reduced model parameters and enabled it better able to be equipped for mobile devices.The accuracy and recall rate of using improved YOLOv5s on the test set by 1.9%and 3.7%compared to 96.2%and 93.7%of YOLOv5s,respectively.The experimental results showed that the average error of monitoring the number of seedlings by unmanned aerial vehicles(UAV)video of rapeseed seedlings based on improved YOLOv5s combined with depth-sort method was 4.3%.The presented approach can realize rapid statistics of the number of rapeseed seedlings in the field based on UAV remote sensing,provide a reference for variety selection and precise management of rapeseed.展开更多
Glaucoma is characterized by the progressive loss of retinal ganglion cells (RGCs),although the pathogenic mechanism remains largely unknown.To study the mechanism and assess RGC degradation,mouse models are often use...Glaucoma is characterized by the progressive loss of retinal ganglion cells (RGCs),although the pathogenic mechanism remains largely unknown.To study the mechanism and assess RGC degradation,mouse models are often used to simulate human glaucoma and specific markers are used to label and quantify RGCs.However,manually counting RGCs is time-consuming and prone to distortion due to subjective bias.Furthermore,semi-automated counting methods can produce significant differences due to different parameters,thereby failing objective evaluation.Here,to improve counting accuracy and efficiency,we developed an automated algorithm based on the improved YOLOv5 model,which uses five channels instead of one,with a squeeze-and-excitation block added.The complete number of RGCs in an intact mouse retina was obtained by dividing the retina into small overlapping areas and counting,and then merging the divided areas using a non-maximum suppression algorithm.The automated quantification results showed very strong correlation (mean Pearson correlation coefficient of 0.993) with manual counting.Importantly,the model achieved an average precision of 0.981.Furthermore,the graphics processing unit (GPU) calculation time for each retina was less than 1 min.The developed software has been uploaded online as a free and convenient tool for studies using mouse models of glaucoma,which should help elucidate disease pathogenesis and potential therapeutics.展开更多
文摘The statistics of the number of rapeseed seedlings are very important for breeders and planters to conduct seed quality testing,field crop management and yield estimation.Calculating the number of seedlings is inefficient and cumbersome in the traditional method.In this study,a method was proposed for efficient detection and calculation of rapeseed seedling number based on improved you only look once version 5(YOLOv5)to identify objects and deep-sort to perform object tracking for rapeseed seedling video.Coordinated attention(CA)mechanism was added to the trunk of the improved YOLOv5s,which made the model more effective in identifying shaded,dense and small rapeseed seedlings.Also,the use of the GSConv module replaced the standard convolution at the neck,reduced model parameters and enabled it better able to be equipped for mobile devices.The accuracy and recall rate of using improved YOLOv5s on the test set by 1.9%and 3.7%compared to 96.2%and 93.7%of YOLOv5s,respectively.The experimental results showed that the average error of monitoring the number of seedlings by unmanned aerial vehicles(UAV)video of rapeseed seedlings based on improved YOLOv5s combined with depth-sort method was 4.3%.The presented approach can realize rapid statistics of the number of rapeseed seedlings in the field based on UAV remote sensing,provide a reference for variety selection and precise management of rapeseed.
基金supported by the National Natural Science Foundation of China (61405028 to J.Z.,81770935 to H.B.Z.)Fundamental Research Funds for the Central Universities (University of Electronic Science and Technology of China)(ZYGX2019J053 to J.Z.)Department of Science and Technology of Sichuan Province,China (2020YJ0445 to H.B.Z.)。
文摘Glaucoma is characterized by the progressive loss of retinal ganglion cells (RGCs),although the pathogenic mechanism remains largely unknown.To study the mechanism and assess RGC degradation,mouse models are often used to simulate human glaucoma and specific markers are used to label and quantify RGCs.However,manually counting RGCs is time-consuming and prone to distortion due to subjective bias.Furthermore,semi-automated counting methods can produce significant differences due to different parameters,thereby failing objective evaluation.Here,to improve counting accuracy and efficiency,we developed an automated algorithm based on the improved YOLOv5 model,which uses five channels instead of one,with a squeeze-and-excitation block added.The complete number of RGCs in an intact mouse retina was obtained by dividing the retina into small overlapping areas and counting,and then merging the divided areas using a non-maximum suppression algorithm.The automated quantification results showed very strong correlation (mean Pearson correlation coefficient of 0.993) with manual counting.Importantly,the model achieved an average precision of 0.981.Furthermore,the graphics processing unit (GPU) calculation time for each retina was less than 1 min.The developed software has been uploaded online as a free and convenient tool for studies using mouse models of glaucoma,which should help elucidate disease pathogenesis and potential therapeutics.
