Traffic Sign Classification Using Transfer Learning: An Investigation of Feature-Combining Model

Abstract

The traffic sign classification system is a technology to help drivers to recognise the traffic sign hence reducing the accident. Many types of learning models have been applied to this technology recently. However, the deployment of learning models is unknown and shown to be non-trivial towards image classification and object detection. The implementation of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features as well as can save lots of training time. Besides, the feature-combining model exhibited great performance in the TL method in many applications. Nonetheless, the utilisation of such methods towards traffic sign classification applications are not yet being evaluated. The present study aims to exploit and investigate the effectiveness of transfer learning feature-combining models, particularly to classify traffic signs. The images were gathered from GTSRB dataset which consists of 10 different types of traffic signs i.e. warning, stop, repair, not enter, traffic light, turn right, speed limit (80km/s), speed limit (50km/s), speed limit (60km/s), and turn left sign board. A total of 7000 images were then split to 70:30 for train and test ratio using a stratified method. The VGG16 and VGG19 TL-features models were used to combine with two classifiers, Random Forest (RF) and Neural Network. In summary, six different pipelines were trained and tested. From the results obtained, the best pipeline was VGG16+VGG19 with RF classifier, which was able to yield an average classification accuracy of 0.9838. The findings showed that the feature-combining model successfully classifies the traffic signs much better than the single TL-feature model. The investigation would be useful for traffic signs classification applications i.e. for ADAS systems