In this blog, we will explore how to effectively detect and classify traffic signs using the Single Shot MultiBox Detector (SSD) in TensorFlow. This implementation is still a work in progress with ongoing efforts to refine its capabilities.

Overview

The goal is to utilize SSD, a powerful deep learning model, to detect specific traffic signs such as stop signs and pedestrian crossings. This implementation has demonstrated an impressive capability of processing 40-45 frames per second (fps) on a GTX 1080 equipped with an Intel Core i7-6700K processor.

The model currently suffers from overfitting. Thus, it’s essential to pre-train on the VOC2012 dataset before transferring the learning to our traffic sign classification task.

Dependencies

• Python 3.5+
• TensorFlow v0.12.0
• Pickle
• OpenCV-Python
• Matplotlib (optional)

Setting Up the Environment

Follow these steps to clone the repository and prepare your environment:

Clone the repository somewhere, let's refer to it as $ROOT

Training the Model from Scratch

To train the model, adhere to the steps below:

1. Download the LISA Traffic Sign Dataset, and store it in a directory referred to as $LISA_DATA.
2. Navigating to the dataset directory:

cd $LISA_DATA

3. Follow the provided instructions in the dataset to create mergedAnnotations.csv containing data only for stop signs and pedestrian crossings.
4. Copy the data-gathering script:

cp $ROOT/data_gathering/create_pickle.py $LISA_DATA

5. Run the script to create pickle files:

python create_pickle.py

6. Navigating back to the ROOT directory:

cd $ROOT

7. Create symbolic links for resized images and raw data:

ln -s $LISA_DATA/resized_images_* .

ln -s $LISA_DATA/data_raw_*.p .

8. Prepare the data:

python data_prep.py

9. This script performs box matching between the ground-truth boxes and default boxes and packages the data.
10. Train the SSD model:

python train.py

11. Run inference on sample images:

python inference.py -m demo

12. To predict your own images or videos, use the -i flag within inference.py.

Understanding the Code: The Market Analogy

Imagine you are at a market, where different stalls represent different traffic signs. You, as a customer (the SSD model), want to quickly identify specific stalls (stop signs and pedestrian crossings) as you move through the market. Instead of stopping at each stall and asking for the wares (which would be akin to checks against many object classes), you generally glance over the stalls (the SSD architecture) designed to catch your eye. The quick identification process mirrors how SSD rapidly detects and classifies the specific objects it has been trained on, highlighting its efficiency and effectiveness.

Performance Metrics

This SSD implementation achieves 40-45 fps during inference, where the neural network inference time is approximately 7-8 ms, complemented by a Non-Maximum Suppression (NMS) time of 15-16 ms. Keep in mind that NMS runs on the CPU and has not been optimized yet.

Improving Model Performance

To enhance model performance, consider the following strategies:

• Pre-train the model on larger datasets like VOC2012 or ILSVRC.
• Implement image data augmentation techniques.
• Engage in hyper-parameter tuning.
• Optimize the NMS algorithm, potentially using existing optimized versions.
• Implement and report the mAP metric.
• Experiment with different base networks and expand to include more traffic sign classes.

Troubleshooting

If the model is overfitting, consider implementing some of the suggested improvements such as data augmentation or using a larger dataset for pre-training. Monitoring the loss and tweaking your training validation split can also help in fine-tuning your model’s performance.

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At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.