Smart Parking and Parking Guidance Using ECNN Algorithm in Convolution Neural Network

Smart Parking and Parking Guidance finding parking methods are in demand in modern megacities. This will reduce traffic, air pollution, driver strain and wasted parking time. This paper presents the design and implementation of an improved version of a CNN based on a parking dataset for finding free parking spaces in parking images. The purpose of this paper is to produce a reliable version with fast generalization that can get information about parking, but it is intended for use with technical cameras that display the number of new parking spaces. This paper provides 94% application accuracy when advertising multiple parking lots. This version also achieved an overall accuracy of 94% while receiving single parking and testicle parking information with a digital camera at an alternative parking lot. Previous reasoning suggests that these pictures should have strong generalizability. These generalizations allow the expert version to be deployed in real parking management structures. It can also serve as the basis for a study to gather new information about parking volumes or add more layers to refine and improve versions.