Efficient plastic categorization for recycling and real-time annotated data collection with TensorFlow object detection model

Abstract

Plastic waste management is the major global issue, and recycling has become a necessary solution to mitigate the impact of plastic waste on the environment. Recycling plastic can significantly reduce pollution by diverting plastic waste from landfills, where it can take hundreds of years to decompose and release harmful chemicals and greenhouse gases. Several systems developed for segregating the municipal solid waste, only few focused on categorizing plastic waste. To address these issues, a plastic waste detection system using TensorFlow pre-trained object detection and MobileNet V2 has been proposed. This work is mainly focused on plastic waste such as PET, HDPE, PVC, LDPE, PP and PS. The proposed system can detect plastic waste category in real time and store the detection information as annotation files in various formats such as json, Pascal voc, and txt. The model saves the detection matrix only when the confidence of prediction is greater than threshold value. This data can be used for fine tuning the model as well as training the new model. To validate the dataset generated by the object detection model, a sample of 54 images annotated by the model is used to train the new model and to ensure that the model is learning from dataset. Furthermore, the proposed system promotes recycling, contributing to the reduction of environmental pollution.