Road Garbage Classification Using ResNet50

Abstract

Nowadays managing the road garbage is essential. The waste disposal leads to pollution, climate change, water contamination etc. The major issue which is unresolved is dealing with the large amount of waste that is dumped in the environment rather than segregating properly. To overcome this problem, a deep learning algorithm is used to segregate the garbage which is beneficial for diminishing landfills, recycling etc. We use One MaxPool layer, one average pool layer, and 48 convolutional layers make up the 50-layer convolutional neural network known as ResNet50. The model that is used to categorise the items has already been trained. In the process of implementation certain stages are involved such as preprocessing, DataAugmentation, training, Finetuning and evaluation of the modal etc. This work aims to keep the environment safe and also helps the municipal corporations to collect garbage effectively in remote areas. The garbage dataset consists of 2,527 images of cardboard, plastic, paper, metal, glass and trash. We achieved an accuracy of 81%. Finally, Precision, Recall, f1 scores and Confusion matrix are calculated with respect to their classes.