Model Description
Context
This YOLOv11 model aims to detect spaces in parking lots, whether filled or empty.
Training Approach
Fine-tuned from a YOLOv11 foundation model using Ultralytics framework. Combined from two public parking lot image datasets, standardized and augmented.
Intended Use Cases
Potential Use Cases:
- Finding busy times and overall trends in parking lot traffic for urban design
- Traffic monitoring apps
- Parking lot owner monitoring
Training Data
Dataset sources
Data taken from currently two of the biggest public parking lot datasets:
- Subset of CNRPark-EXT 1208 images (out of 150,000 total images, info on full dataset can be found here)
- PKLot 12545 images
Combined total 13350 (a few removed due to annotation issues)
Class distribution
| 0 (empty parking space) | 1 (filled) |
|---|---|
| 395,753 | 358,538 |
Limitation: not all instances of classes are annotated in images; some both empty and filled parking spaces missed (see below)
Training Procedure
Standardization:
- Resized to letterbox 256x256
- Grayscale
- Auto-Adjust Contrast: Adaptive Equalization
Augmentations (done to simulate snow, fog, more camera angles):
- Rotation: +/- 15°
- Blur: Up to 1.5px
- Noise: Up to 2% px
Split was initally 70-20-10, but train tripled due to annotations:
| Train | Valid | Test |
|---|---|---|
| 28086 | 2537 | 1451 |
Training specs:
- Framework: Ultralytics
- Hardware: Used Google Collab, T14
- 15 Epochs, batch size 64, took .7 hours
Evaluation Results
Key Visualizations:
Performance Analysis:
Model performs extremely well in all metrics, however -
confusion matrix shows false positives with background ~50% of the time…
Possibly a combination of causes:
- Some parking spaces weren’t annotated as show in examples
- Over-standardization or annotations? Possible culprit: 256x256 images+grayscale+Adaptive Equalization = lots of potential for class information loss
- Under-training (Only did 15 epochs)
Limitations and Biases
Known failure cases: Model struggles with distinguishing background from classes. This is a major concern to consider in using this model.
Data biases: Biased toward high visibility, parking lots with well defined paint as datasets only contain similar data to this. Additionally, parking lot data from Italy & Brazil only. Other countries' parking lots may appear different.
Contextual limitations: Aim for aerial view of parking lot with good visibility.
Inappropriate use cases: Bad visibility, weather, non-aerial camera angles. Low contrast, ambiguous environments.
Ethical considerations: Check with lot owners before setting up cameras to monitor lots.
Sample size limitations: Limitation on snowy, foggy conditions.