Automatic Parking System Using Deep Learning and Internet of Things

To address the labour - intensive requirements of traditional parking lot operations, we have developed an automated system for access control, occupancy tracking, and revenue processing. The proposed system employs computer vision and deep learning techniques to autonomously handle parking lot ingress and egress while reducing operating costs. Vehicles entering and exiting the automated parking facility will be detected using infrared sensors interfaced to a Raspberry Pi microcomputer. License plate images will be captured by a Pi camera module and processed in real-time using an optical character recognition (OCR) algorithm based on a convolutional recurrent neural network architecture optimized for number plate recognition. Human access control can be handled in parallel by leveraging object detection models like Faster R-CNN or YOLO to differentiate pedestrian from vehicle ingress. The onboard occupancy tracking functionality will alleviate the need for manual counting of available spaces. Additionally, real-time parking availability stats will be published to a monitoring website allowing customers to view open slots prior to arrival. Lot capacity constraints can be configured to automatically disable entrance gates when maximum capacity is reached. The system's sensor fusion and computer vision capabilities eliminate tedious manual tallying and oversight required by legacy parking systems. By autonomously controlling access gates, tracking and publishing occupancy stats, and extracting revenue data from license plates, the proposed automated parking lot system aims to reduce operating costs while creating a scalable solution applicable to facilities ranging from apartment complexes to large shopping malls. The use of a low-cost, open-source development platform promotes maintainability while allowing for customized enhancements.