A Deep Learning Framework for Smart Street Cleaning

Abstract

Conventional street cleaning methods include street sweepers going to various spots in the city and manually verifying if the street needs cleaning and taking action if required. However, this method is not optimized and demands a huge investment in terms of time and money. This paper introduces an automated framework which addresses street cleaning problem in a better way by making use of modern equipment with cameras and computational techniques to analyze, find and efficiently schedule clean-up crews for the areas requiring more attention. Deep learning-based neural network techniques can be used to achieve better accuracy and performance for object detection and classification than conventional machine learning algorithms for large volume of images. The proposed framework for street cleaning leverages the deep learning algorithm pipeline to analyze the street photographs and determines if the streets are dirty by detecting litter objects. The pipeline further determines the the degree to which the streets are littered by classifying the litter objects detected in earlier stages. The framework also provides information on the cleanliness status of the streets on a dashboard updated in real-time. Such framework can prove effective in reducing resource consumption and overall operational cost involved in street cleaning.