Mehran970 / Real-Time-Traffic-Sign-Detection

Traffic sign detection by Tensorflow object detection

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:

• faster_rcnn_inception_resnet_v2_atrous
• faster_rcnn_inception_v2
• faster_rcnn_resnet_101
• faster_rcnn_resnet50
• rfcn_resnet101
• ssd_inception_v2
• ssd_mobilenet_v1

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