A deep transfer learning based approach to detect COVID-19 waste

COVID-19 or Novel Coronavirus disease is not only creating a pandemic but also created another kind of problem, initiating a group of wastes, which is also called as COVID-19 waste. COVID-19 waste includes the mask, hand gloves, sanitizer bottles, Personal Protective Equipment (PPE) kits, syringes used to vaccinate people, etc. These wastes are now polluting every continent and ocean. Improper disposal of such wastes may increase the rate of spread of contamination. In this regard, we decided to build up a detection model, which will be able to detect some of the COVID-19 wastes. We considered masks, hand gloves, and syringes as the initial wastes to get detected. We collected the dataset manually, annotated the images with these three classes, then trained different CNN models to compare the accuracies of the models for our dataset. We got the best model to be EfficientDet D0, which gives a mean average precision of 0.82. Further, we have also developed a UI to deploy the model, where general users can upload the images and can detect the wastes, controlling the threshold.

Automated summary

COVID-19 waste, including masks, gloves, and sanitizer bottles, is polluting the environment. To address the waste detection issue, researchers built a detection model EfficientDet D0 and developed a UI for users to upload images for waste detection.