Garbage Detection and Classification using Faster-RCNN with Inception-V2

Abstract

Street garbage monitoring is important for ensuring a clean and healthy civic environment. In this context, automatic detection and classification of waste/litter objects on the streets is necessary for proper garbage disposal. Specifically, such detection and classification of waste objects could be utilized in developing automated waste sorting applications. This paper attempts to build a Faster R-CNN based predictive model for automatic classification of ten different types of waste/litter objects. Two pre-trained networks namely Inception-V2 and ResNet-101 are investigated as backbone networks for feature extraction. The performance of the proposed model is also compared with two baselines namely RFCN (region-based fully convolutional network) and SSD (single shot multibox detector). It is observed that the Faster R-CNN configured with InceptionV2 achieves the highest mAP (mean average precision) of 92%.