Fast assessment of the initial carbon to nitrogen ratio(C/N)of organic fraction of municipal solid waste(OFMSW)is an important prerequisite for automatic composting control to improve efficiency and stability of the b...Fast assessment of the initial carbon to nitrogen ratio(C/N)of organic fraction of municipal solid waste(OFMSW)is an important prerequisite for automatic composting control to improve efficiency and stability of the bioconversion process.In this study,a novel approach was proposed to estimate the C/N of OFMSW,where an instance segmentation model was applied to predict the masks for the waste images.Then,by combining the instance segmentation model with the depth-camera-based volume calculation algorithm,the volumes occupied by each type of waste were obtained,therefore the C/N could be estimated based on the properties of each type of waste.First,an instance segmentation dataset including three common classes of OFMSW was built to train mask region-based convolutional neural networks(Mask R-CNN)model.Second,a volume measurement algorithm was proposed,where the measurement result of the object was derived by accumulating the volumes of small rectangular cuboids whose bottom area was calculated with the projection property.Then the calculated volume was corrected with linear regression models.The results showed that the trained instance segmentation model performed well with average precision scores AP_(50)=82.9,AP_(75)=72.5,and mask intersection over unit(Mask IoU)=45.1.A high correlation was found between the estimated C/N and the ground truth with a coefficient of determination R2=0.97 and root mean square error RMSE=0.10.The relative average error was 0.42%and the maximum error was only 1.71%,which indicated this approach has potential for practical applications.展开更多
基金funded by the National Key Research and Development Program of China(Grant No.2018YFD0200800)Key Research and Development Program of Hunan Province(Grant No.2018GK2013)+1 种基金Hunan Modern Agricultural Industry Technology Program(Grant No.201926)Innovation and Entrepreneurship Training Program of Hunan Agricultural University(Grant No.2019062x).
文摘Fast assessment of the initial carbon to nitrogen ratio(C/N)of organic fraction of municipal solid waste(OFMSW)is an important prerequisite for automatic composting control to improve efficiency and stability of the bioconversion process.In this study,a novel approach was proposed to estimate the C/N of OFMSW,where an instance segmentation model was applied to predict the masks for the waste images.Then,by combining the instance segmentation model with the depth-camera-based volume calculation algorithm,the volumes occupied by each type of waste were obtained,therefore the C/N could be estimated based on the properties of each type of waste.First,an instance segmentation dataset including three common classes of OFMSW was built to train mask region-based convolutional neural networks(Mask R-CNN)model.Second,a volume measurement algorithm was proposed,where the measurement result of the object was derived by accumulating the volumes of small rectangular cuboids whose bottom area was calculated with the projection property.Then the calculated volume was corrected with linear regression models.The results showed that the trained instance segmentation model performed well with average precision scores AP_(50)=82.9,AP_(75)=72.5,and mask intersection over unit(Mask IoU)=45.1.A high correlation was found between the estimated C/N and the ground truth with a coefficient of determination R2=0.97 and root mean square error RMSE=0.10.The relative average error was 0.42%and the maximum error was only 1.71%,which indicated this approach has potential for practical applications.
