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Real-Time-Traffic-Sign-Detection

Traffic sign detection by Tensorflow object detection

Requirements

This project is implemented in Tensorflow 2 and it is based on Tensorflow Object Detection API.

For install Tensorflow 2, just use this command:

pip install tensorflow==2.0.0 

Pretrained models

You can find models from following links and put them inside "models" directory:

for example:

├── models
│   ├── faster_rcnn_inception_resnet_v2_atrous
│   ├── ssd_mobilenet_v1

Note modify default model name in line 34 of code as name of model in models directory.

Results

Acccording to Evaluation of deep neural networks for traffic sign detection systems paper: Faster R-CNN Inception Resnet V2 model obtains the best mAP, while R-FCN Resnet 101 strikes the best trade-off between accuracy and execution time. SSD Mobilenet is the fastest and the lightest model in terms of memory consumption, making it an optimal choice for deployment in mobile and embedded devices.

model mAP parameters flops memory_mb total_exec_millis accelerator_exec_millis cpu_exec_millis
Faster R-CNN Resnet 50 91.52 43337242 533575386662 5256.454615 104.0363553 75.93395395 28.10240132
Faster R-CNN Resnet 101 95.08 62381593 625779295782 6134.705805 123.2729175 90.33714433 32.9357732
Faster R-CNN Inception V2 90.62 12891249 120621363525 2175.206857 58.53338971 38.76813971 19.76525
Faster R-CNN Inception Resnet V2 95.77 59412281 1837544257834 18250.446008 442.2206796 366.1586796 76062
R-FCN Resnet 101 95.15 64594585 269898731281 3509.75153 85.45207971 52.40321739 33.04886232
SSD Mobilenet 61.64 5572809 2300721483 94.696119 15.14525 4.021267857 11.12398214
SSD Inception V2 66.10 13474849 7594247747 284.512918 23.74428378 9.393405405 14.35087838

Run

Just running:

 ts_real_time.py

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