Object Detection and Image Processing for Parking Management
Parking management is a crucial aspect of urban planning, and it can be challenging to efficiently manage parking spaces in busy cities. To address this problem, researchers combined object detection and image processing techniques with deep learning algorithms like YOLOv8. This combination enables the system to detect and recognize objects like vehicles and parking slots in images of parking lots.
Dataset Creation and Split
To evaluate the performance of the object detection model, a dataset of labeled images was created. The dataset contained three sets: training, validation, and test sets, with each set containing 80%, 20%, and 20% of the images respectively. This division ensures that the model is trained fairly and accurately represents its performance.
Quantitative Evaluation and Comparison
To evaluate the performance of different object detection models, quantitative evaluations were conducted on the validation and test sets. The results showed that YOLOv8 outperformed other models like Faster R-CNN and YOLOv5 in terms of detection accuracy and computational efficiency. This demonstrates that YOLOv8 is a state-of-the-art approach for object detection in parking management applications.
Image Processing Techniques for Parking Slot Detection
To detect parking slots, image processing techniques like edge detection, thresholding, contour detection, and morphological operations are employed. These techniques help differentiate parking slots from the surrounding environment or vehicles based on visual cues. By analyzing these characteristics, the system can accurately identify parking slots in images of parking lots.
Firebase Database for Record-Keeping
To streamline record-keeping, a NoSQL database called Firebase is used. This database offers several benefits, including flexibility, scalability, and offline support, ensuring that data remains available even when the device is not connected to the internet.
Conclusion
In conclusion, object detection and image processing techniques combined with deep learning algorithms like YOLOv8 can efficiently manage parking spaces in busy cities. The system can detect and recognize objects like vehicles and parking slots in images of parking lots, making it easier to identify available parking spots. Additionally, the use of Firebase database ensures reliable record-keeping and offline data availability. By implementing these techniques, urban planners and policymakers can create more efficient and sustainable parking management systems.
