Deep Learning Framework for Smart Street Cleaning

Now days each and every city is becoming smart city to attain these condition major criteria is cleanliness. Street cleaning includes street sweepers and it requires sweepers to present manually in particular location and to take action based on need. Efficient cleaning is not possible through this method which consumes more time and money. To address this issue based on technology development most of the cities has been implementing camera in terms of security. Utilizing this camera’s street garbage is identified and its current condition is detected with its location also extracted. Deep learning-based neural network techniques can be used to achieve better accuracy and performance for object detection and classification for large volume of images. Based on the images collected from camera’s was viewed and necessary action should taken by respective in-charged person to city administrator. A cleaned garbage image should be updated by that person for proof. In case update is not done then it will reported to higher officers. All these details were updated in cloud for efficient storage utilization. Such framework can prove effective in reducing resource consumption and overall operational cost involved in street cleaning.