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Analysis of Parking Spaces

Structure

.
├── network/
|   ├── alexnet.py
|   ├── lenet.py
|   ├── vgg7.py
|   ├── vgg13.py
|   ├── vgg13v.py
|   └── vgg16.py
├── src_img/
|   ├── PUCPR/
|   ├── UFPR04/
|   └── UFPR05/
├── train_data/
|   ├── models/
|   ├── test_img/
|   ├── test_seg/
|   └── train/
├── calc_accuracy.py
├── data_generator.py
├── detect_contour.py
├── test_image.py
├── test_segment.py
├── train_network.py
└── README.md

Hardware requirements

NVIDIA® GPU card with CUDA® Compute Capability 3.5 or higher.

Software requirements

Datasets

Procedure

0. Debug

Related files:

  • test_segment.py
  • train_data/models/
  • train_data/test_seg/

Parameters type: cli arguments

Execute:

python test_segment.py -m train_data/models/vgg7/pucpr-200.model -d train_data/test_seg/

Related files:

  • detect_contour.py
  • train_data/test_img/

Parameters type: built-in

Execute:

python detect_contour.py

1. Prepare

Related files:

  • data_generator.py
  • src_img/
  • train_data/train/

Parameters type: built-in

Execute:

python data_generator.py

2. Train

Related files:

  • train_network.py
  • train_data/train/
  • train_data/models/

Parameters type: cli arguments

Execute:

# All datasets
python train_network.py -i train_data/train/ -o train_data/models/vgg7/all

# Specific dataset
python train_network.py -i train_data/train/pucpr -o train_data/models/vgg7/pucpr

3. Assess

Related files:

  • test_image.py
  • calc_accuracy.py
  • train_data/models/
  • train_data/test_img/

Parameters type: built-in

Execute:

# Test image
python test_image.py

# Calc accuracy
python calc_accuracy.py

Accuracy

How to decide training size:

PUCPR 36 days, UFPR04 31 days, UFPR05 33 days

About 12 hours (6am - 6pm), 12 sheets per hour (Take one shot every 5 minutes)

12x12x0.2=28.8, 33x0.2=6.6, 28.8x6.6=190.08

Parameters:

Testing size: 20,000 parking spaces on each parking lot

Training images: 200

Epochs: 5

Parking lot Network Acc (PUCPR) Acc (UFPR04) Acc (UFPR05)
PUCPR AlexNet 99.87% 98.06% 94.69%
PUCPR LeNet 99.84% 97.56% 84.78%
PUCPR VGG-7 99.95% 99.02% 95.77%
PUCPR VGG-13 99.93% 94.28% 92.81%
PUCPR VGG-13V 99.97% 98.37% 95.13%
PUCPR VGG-16 99.77% 96.79% 92.13%
ALL AlexNet 99.44% 93.01% 98.52%
ALL LeNet 99.58% 97.27% 98.83%
ALL VGG-7 99.75% 98.80% 99.57%
ALL VGG-13V 99.67% 98.79% 99.07%
ALL VGG-16 93.20% 77.69% 98.87%

Epochs: 5

Parking lot Network Images Acc (PUCPR) Acc (UFPR04) Acc (UFPR05)
PUCPR VGG-7 100 89.72% 49.41% 88.92%
PUCPR VGG-7 200 99.95% 99.02% 95.77%
PUCPR VGG-7 1000 99.99% 98.17% 96.25%

Training images: 200

Parking lot Network Epochs Acc (PUCPR) Acc (UFPR04) Acc (UFPR05)
ALL VGG-16 5 93.20% 77.69% 98.87%
ALL VGG-16 10 98.08% 92.81% 96.76%
ALL VGG-16 15 97.48% 95.17% 98.05%

Training images: 800

Parking lot Network Epochs Acc (PUCPR) Acc (UFPR04) ACC (UFPR05)
ALL VGG-7 5 99.94% 99.09% 99.44%
ALL VGG-7 8 99.69% 98.47% 99.30%
ALL VGG-7 12 99.99% 99.72% 99.52%
Parking lot Network Epochs Acc (PUCPR) Acc (UFPR04) ACC (UFPR05)
ALL VGG-13V 5
ALL VGG-13V 10
ALL VGG-13V 20 99.95% 99.63% 99.38%

Regularization

Use PUCPR training set & VGG-7 network

Id Method Acc (PUCPR) Acc (UFPR04) Acc (UFPR05)
r0 None 99.81% 92.55% 92.53%
r1 Batch Normalization 99.94% 96.73% 91.02%
r2 L2 regularization 99.43% 91.27% 77.62%
r3 Dropout 99.95% 96.95% 92.84%
r4 BN + Dropout 99.95% 99.02% 95.77%
r5 BN + L2 + Dropout 99.87% 92.51% 94.45%

Use PUCPR training set & VGG-13V network

Id Method Acc (PUCPR) Acc (UFPR04) Acc (UFPR05)
r0 None 99.97% 98.69% 89.49%
r1 Batch Normalization 99.94% 97.48% 96.37%
r2 L2 regularization 99.91% 96.14% 93.39%
r3 Dropout 55.12% 55.67% 42.29%
r4 BN + Dropout 99.82% 94.05% 93.66%
r5 BN + L2 + Dropout 99.97% 98.37% 95.13%

Verbose

train_network.py

(Tensorflow-3.6) C:\Users\AWAKE\Desktop\GP\ips>python train_network.py -d train_data/train/ -m train_data/models/vgg16-200.model
Using TensorFlow backend.
[INFO] loading images...
[INFO] compiling model...
2019-03-30 14:43:39.127451: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.96GiB
2019-03-30 14:43:39.135581: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-03-30 14:43:40.248390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-30 14:43:40.253854: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-03-30 14:43:40.256276: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-03-30 14:43:40.260234: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4714 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
[INFO] training network...
Epoch 1/5
819/819 [==============================] - 95s 116ms/step - loss: 0.2855 - acc: 0.9539 - val_loss: 0.1071 - val_acc: 0.9681
Epoch 2/5
819/819 [==============================] - 87s 107ms/step - loss: 0.0765 - acc: 0.9789 - val_loss: 7.0363 - val_acc: 0.5226
Epoch 3/5
819/819 [==============================] - 87s 107ms/step - loss: 0.1046 - acc: 0.9717 - val_loss: 0.0206 - val_acc: 0.9933
Epoch 4/5
819/819 [==============================] - 89s 108ms/step - loss: 0.0598 - acc: 0.9838 - val_loss: 0.0252 - val_acc: 0.9931
Epoch 5/5
819/819 [==============================] - 94s 115ms/step - loss: 0.0407 - acc: 0.9885 - val_loss: 0.2214 - val_acc: 0.9199
[INFO] serializing network...

calc_accuracy.py

(Tensorflow-3.6) C:\Users\AWAKE\Desktop\GP\ips>python calc_accuracy.py
Using TensorFlow backend.
[INFO] loading network...
2019-03-31 20:00:34.046661: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.96GiB
2019-03-31 20:00:34.055607: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-03-31 20:00:35.094689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-31 20:00:35.100110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-03-31 20:00:35.103378: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-03-31 20:00:35.107615: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4714 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
100%|#######################################################################################################| 60000/60000 [05:02<00:00, 198.42it/s]
Accuracy of PUCPR: 98.87%
Accuracy of UFPR04: 95.65%
Accuracy of UFPR05: 86.81%

References