Parking Lot Occupancy Detection using Deep Learning and Fisheye Camera for AIoT System
The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational l...
Saved in:
| Published in | Korean Institute of Smart Media Vol. 13; no. 1; pp. 24 - 35 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
(사)한국스마트미디어학회
31.01.2024
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2287-1322 2288-9671 |
| DOI | 10.30693/SMJ.2023.13.01.24 |
Cover
| Summary: | The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space. |
|---|---|
| ISSN: | 2287-1322 2288-9671 |
| DOI: | 10.30693/SMJ.2023.13.01.24 |