Clean Streets: A Deep Learning Framework Advisor: Dr. Magdalini Eirinaki Chandni Balchandani, Rakshith Koravadi Hatwar, Parteek Makkar, Yanki Shah, Pooja Yelure Department of Computer Engineering, San José State University, San Jose, CA Contributions Server Architecture Result and Analysis Abstract A framework to automate street cleaning. Leverages deep learning to analyze street photographs and determine if the streets are dirty by detecting litter objects. Classifies the litter objects. Provides information on the cleanliness status of the streets on a dashboard updated in real-time. Result and Analysis The graph shown above displays phase wise performance of pipeline for images with varying level of cleanliness. Red bar – Phase 1, Orange – Phase 2 and Green – Phase 3. The graph above displays cleanliness score computed for 10 clean streets. Correct classified instances : 8 Accuracy = 8/10 = 80% The graph above displays cleanliness score computed for 8 streets with little litter(dirty streets). Correct classified instances : 5 Accuracy = 5/8 = 62.5% The graph above displays cleanliness score computed for 10 streets with a lot of litter (very dirty streets). Motivation Currently street cleaning is done by the city based on a predifined schedule and not on necessity leading to inefficient use of resources. Clean Streets analyses the images and gives a cleanliness score. The Framework automates and optimizes the cleaning schedule based on priority and cleanliness score of the streets. Hence, reduces the operational cost and optimizes resource allocation. Original Image Phase 1 Methodology Methodology Distributed hybrid deep learning based image processing pipeline with Framework/Model Agnostic Phases. On-demand scaling based input images volume. Easily extend object clusters/classes to detect new objects. Embed localization and classification information inside image metadata. Step-by-Step Phase Visualization. Cleanliness score calculated as ratio of total bounding box area to the street area Image pipeline available as REST API for easy integration with existing systems. Conclusion Conclusion This project presents a reliable deep learning-based framework that allows for automatic litter detection and classification. Enables real-time monitoring of the street's cleanliness. This is the first step towards developing a holistic and efficient automated system to manage cleaning activities as a part of the Smart City initiative for the city of San Jose. Phase 3 Phase 2