Automatic Vacant Parking Places Management System Using Multicamera Vehicle Detection
Banoth Veena
Computer Science and
Engineering (Internet Of Things)
Guru Nanak Institutions
Technical Campus
Telangana, India veenabanoth52@gmail.com
Muthe Sachin
Computer Science and
Engineering
(Internet Of Things)
Guru Nanak Institutions
Technical Campus
Telangana, India
sachinmuthe2004@gmail.com
Sudanapu Achyuth Kumar
Computer Science and
Engineering
(Internet Of Things)
Guru Nanak Institutions
Technical Campus Telangana, India
sudanapuachyuthkumar@gmail
.com
B Surekha
(Assistant Professor)
Computer Science and Engineering
GuruNanakInstitutions
Technical Campus
Telangana, India
Surekhait21@gmail.com
Abstract-- Managing vacant parking spaces efficiently is crucial for smart city applications. This paper tackles the challenge by introducing a deep neural network approach for detecting parking occupancy. For large parking areas, using a single high-mounted camera offers an effective way to monitor the entire space. To achieve this, we leveraged the well-known YOLO object detection model, which is recognized for its high precision and real-time speed. We propose a modified, lightweight version of the YOLO-v5 architecture tailored for parking management. Our model is designed to detect vehicles of various sizes, from large trucks to small cars, using a multi-scale learning mechanism. This approach enables the model to learn detailed, discriminative features across different scales. By optimizing the architecture, we reduced the number of trainable parameters from 7.28 million in YOLO-v5S to 7.26 million in our model while significantly boosting precision. Our model achieves a detection speed of 30 frames per second (fps) and outperforms the larger YOLO-v5-L and YOLO-v5-X versions, particularly excelling in detecting small vehicles, with a 33% improvement compared to YOLO-v5-X. Experimental results demonstrate that our approach is both efficient and accurate, making it a promising solution for managing parking spaces autonomously in smart cities.
Keywords--It deals with smart parking management, parking occupancy detection, YOLO-v5 architecture, deep neural networks, smart cities, vehicle detection, cenital-plane camera, real-time object detection, multi-scale mechanism, tiny vehicle detection, deep learning applications, parking space optimization, computer vision, traffic optimization, machine learning models for parking, low-end terminals deployment, parking resource management, modified YOLO-v5 model, autonomous parking systems, and urban mobility solutions.
