Modified rat swarm optimization with deep learning model for robust recycling object detection and classification

Biomass residues consist of sewage effluents and sludges, crop wastes, non-recyclable municipal solid waste industrial and domestic greywater, and much more. Recycling is regarded as one such significant disposal technique. Considering the intelligent classification and detection of solid waste as an essential factor in consumption and recycling, multi-object solid waste identification and classification methodology on the basis of transfer learning (TL) are presented. This study develops a Modified Rat Swarm Optimization with Deep Learning for Robust Recycling Object Detection and Classification (MRSODL-RODC) model. Primarily, fully convolutional network (FCN) is applied for the identification of waste objects. Moreover, MRSO with deep belief network (DBN) model is applied for object detection process. The design of the MRSO algorithm for the DBN technique, showing the novelty of our work. The performance validation of the MRSODL-RODC model can be executed with the help of the benchmark dataset from Kaggle repository. The experimental outcomes demonstrated the better performance of the MRSODL-RODC model over recent approaches with higher accuracy of 99.20%.