Jingjing Huang;Hongduo Zhang;Xu Xiao;Jingqi Huang;Jingxin Xie;Liang Zhang;Heming Hu;Sihui Dai;Ming Li;Yang X

College of Mechanical & Electronical Engineering, Hunan Agricultural University, Changsha 410128, China.;Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan;Graduate School of Agricultural and Life Sciences, the University of Tokyo, Tokyo 113-0033, Japan.;Hunan Agricultural Equipment Research Institute, Changsha 410125, China.

depth camera;segmentation model;instance;estimation;mask r-cnn;mas

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 R- (2)=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.