Method for C/N ratio estimation using Mask R-CNN and a depth camera for organic fraction of municipal solid wastes

Abstract

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.

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

“…All these algorithms were adopted in solid waste classification shortly after having been launched. Relevant practices are presented in Table , and wastes were involved which included domestic wastes, CDW, and WEEE − and achieved inspired results. Faster R-CNN and Mask R-CNN have been prevailing because of high accuracy and detection speed.…”

“…Faster R-CNN and Mask R-CNN have been prevailing because of high accuracy and detection speed. The detection speed of mask R-CNN with ResNet or MobileNet achieved > 40 fps . Besides, Faster R-CNN and Mask R-CNN have good performance in detecting small objects, like screws and nails. , Mask R-CNN with a MobileNet backbone can be well trained with limited hardware …”