Comparison of Computer Vision and Convolutional Neural Networks for Vehicle Parking Control
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26 giu 2025
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 26 giu 2025
Pagine: 26 - 33
Ricevuto: 14 nov 2024
Accettato: 04 apr 2025
DOI: https://doi.org/10.14313/jamris-2025-011
Parole chiave
© 2025 Jonathan Aguilar Alvarado et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Training parameters
| Params | Value |
|---|---|
| Datasets | own |
| train images | 800 |
| validation images | 200 |
| Learning rate | 0.001 |
| Pre-trained weights | yolov5x.pt |
| Number of epochs | 500 |
| Batch size | 8 |
| Image dimensions (height x width) | 640 × 640 |
Materials used in research
| Tool | Description |
|---|---|
| DAHUA IPC-HFW1430DT-STW | 4 MP, 2.8 mm fixed lens, 1/3” progressive CMOS sensor, H.265+ compression, 30M IR LED, DWDR, Day/Night mode (ICR), 3DNR, AWB, AGC, BLC, Mirror, IP67 outdoor protection, WiFi, MicroSD slot (256 GB) |
| Google Colab | Cloud-based execution and training environment with GPU support. |
| LabelImg | Open-source tool for manual image labeling |
| Roboflow | Software for organizing, labeling, and transforming images. |
| YAML | Text file format for model parameter configuration |
Computer vision metrics result
| Precision | Sensibility | |
|---|---|---|
| 1 | 1 | |
| 0 | 0 | |
| 1 | 1 | |
| 1 | 0.94 | |
| 1 | 1 | |
| 0.80 | 0.79 |
Comparison of accuracy and sensitivity of different parking space detection methods
| Research Study | Technique | Precision | Sensibility |
|---|---|---|---|
| This Study | YOLO V5 (CNN) | 88.00% | 82.00% |
| [ |
DeepLabV3+ | 77.26% | 79.55% (Dice) |
| [ |
YOLO (CNN, pixel-wise ROI) | 99.68% (balanced accuracy) | 99.68% (balanced accuracy) |
| [ |
ResNet50 + SVM VGG16 | 98.90% 93.40% | Not specified |
| [ |
Semantic Segmentation (CNN) | 96.81% | 97.80% |
| [ |
mAlexNet (CNN) | 90.34% | 98.98% |
| [ |
YOLO V4 (CNN) | 93.00% | 98.00% |
| [ |
U-Net (CNN) | 99.40% | 92.94% |
| [ |
YOLOv7 + IoU (CNN) | 90.04% | 82.17% |
| This Study | Image Segmentation (CV) | 80.00% | 79.00% |
| [ |
Optical Flow (CV) | 98.80% | 94.40% |
| [ |
HOG, LBP, SVM y Naive Bayes (CV) | 97.00% | 97.00% |
| [ |
Binary Morphology y Logic (CV) | 76.75% | 99.00% |
| [ |
Optical Flow (CV) | 97.90% | 62.40% |
| [ |
Block Matching Algorithm (CV) | 93.00% | 46.00% |
| [ |
Multi-clue recovery model (CV) | 93.21% | 96.84% |
CNN metrics result
| Metric | Value |
|---|---|
| Precision | 0.8755 |
| Sensibility | 0.8158 |