# 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](https://universe.roboflow.com/autonomousparking/cnrpark-ext-cnziv-f7ygy) 1208 images (out of 150,000 total images, info on full dataset can be found [here](http://cnrpark.it/))
- [PKLot](https://public.roboflow.com/object-detection/pklot/1) 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)
<img alt="Example Image 1" src="example.png" width="400"/>
<img alt="Example Image 2" src="example2.png" width="400"/>

# 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
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**Key Visualizations:**

<img alt="Confusion Matrix" src="confusion_matrix_normalized.png" width="700"/>
<img alt="F1-Confidence Graph" src="BoxF1_curve.png" width="700"/>
<img alt="Precision-Confidence Graph" src="BoxP_curve.png" width="700"/>

**Performance Analysis:**
<!--
This is where you demonstrate critical thinking. Interpret your results - don't just present numbers
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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.