Multiple Garbage Detection using Mask R-CNN in TensorFlow for Household Waste Classification

At the moment, 2.01 billion tons of solid trash are produced annually in all cities throughout the world. Based on research findings, a typical person generates approximately 0.74 kilograms of garbage on a daily basis. The researchers decided to help the community by providing information on household waste detection and classification. By means of implementing the Mask R-CNN framework, it became feasible to obtain a segmented mask as well as a bounding box for every identified household waste item in a photo. Bounding boxes were used to identify multiple wastes, things with different scales, and objects that overlap in an image. Bounding boxes were made of various household wastes. The wastes were classified into biodegradable, non-biodegradable, and recyclable which has the potential to mitigate human labor and expedite the waste segregation process. The Mask R-CNN model was used to improve detection and demonstrate how household trash may be appropriately identified in a photo with several objects. The model for detecting objects encountered challenges in detecting household wastes caused by their deformity, cluttered and occluded environment, and poor lighting conditions. The Mask R-CNN model produced a 38.25% mean average precision, 65.50% mean average recall, and 48.29% F1 score. The proposed model can be utilized in detecting and classifying household wastes.