Real-Time Car Parking Detection with Deep Learning in Different Lighting Scenarios

Abstract

This paper presents an intelligent parking system utilizing image processing and deep learning to address parking challenges amidst varying lighting conditions. The escalating number of vehicles on the road increased the difficulty and time spent in finding available parking spaces, resulting in more cars congestion in the modern cities. To alleviate this issue, we propose an efficient real-time camera-based system that is capable of detection of open parking slots using deep learning methodologies. Initially, we introduce a simple parking detection technique utilizing image processing. Nevertheless, it proves ineffective in dim lighting. Subsequently, we introduce our AI-powered system, trained on the "COCO" dataset using the object detection deep learning YOLO algorithm. The used dataset has been applied with a large-scale collection of images annotated with object categories, bounding boxes, segmentation masks, and captions. It is shown that this solution accurately identifies available and occupied parking slots by detecting vehicles within the parking area. We proposed strategically positioned webcam that provides comprehensive coverage of the parking area, to be set as an initial image serving as a reference for identifying all parking slots. During operation, the webcam records real-time video footage of the parking area, enabling continuous updates for an accurate count of free and occupied parking slots. The paper details the step-by-step implementation of the system and showcases achieved results under diverse lighting conditions. In conclusion, this research demonstrates the system's effectiveness in mitigating parking challenges through the amalgamation of image processing, deep learning, and real-time video analysis. Additionally, we highlight the future potential for research to further enhance and advance this innovative system.