Traffic sign classification using transfer learning: An investigation of feature-combining model

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 system

Unconstrained Road Sign Recognition

There are many types of road signs, each of which carries a different meaning and function: some signs regulate traffic, others indicate the state of the road or guide and warn drivers and pedestrians. Existent image-based road sign recognition systems work well under ideal conditions, but experience problems when the lighting conditions are poor or the signs are partially occluded. The aim of this research is to propose techniques to recognize road signs in a real outdoor environment, especially to deal with poor lighting and partially occluded road signs. To achieve this, hybrid segmentation and classification algorithms are proposed. In the first part of the thesis, we propose a hybrid dynamic threshold colour segmentation algorithm based on histogram analysis. A dynamic threshold is very important in road sign segmentation, since road sign colours may change throughout the day due to environmental conditions. In the second part, we propose a geometrical shape symmetry detection and reconstruction algorithm to detect and reconstruct the shape of the sign when it is partially occluded. This algorithm is robust to scale changes and rotations. The last part of this thesis deals with feature extraction and classification. We propose a hybrid feature vector based on histograms of oriented gradients, local binary patterns, and the scale-invariant feature transform. This vector is fed into a classifier that combines a Support Vector Machine (SVM) using a Random Forest and a hybrid SVM k-Nearest Neighbours (kNN) classifier. The overall method proposed in this thesis shows a high accuracy rate of 99.4% in ideal conditions, 98.6% in noisy and fading conditions, 98.4% in poor lighting conditions, and 92.5% for partially occluded road signs on the GRAMUAH traffic signs dataset

Hierarchical Traffic Sign Recognition by Super-Resolution Transfer

道路交通标志识别系统是智能交通系统的重要的组成部分。在无人驾驶、驾驶辅助系统、道路资源控制系统等领域，道路交通标志识别的研究都有着非常重要的实际应用价值和科学研究意义。然而，自然场景中的交通标志受光照、视点及遮挡等因素影响极大，多类交通标志的识别依然是一个极具挑战性的问题，特别是面向实际应用的多类交通标志识别对算法的实时性和识别精度都有极高的要求，是解决交通标志识别问题的瓶颈。为了克服这些困难，本文主要针对以下两点进行研究：（1）数据不平衡的多类交通标志分类问题。（2）小尺度交通标志的多类分类问题。本文的主要贡献如下： （1）提出了一种层次化的交通标志识别方法。交通标志类别多且数据不平衡，采...Traffic Sign Recognition (TSR) is a significant part of Intelligent Transportation System, having already played an important role in manless driving system, driver assistance system, and road resource control system. The research of traffic sign recognition contributes immeasurably to both practical application and scientific study. However, the traffic signs in the natural scenes are greatly inf...学位：工学硕士院系专业：信息科学与技术学院_工程硕士(计算机技术)学号：2302014115320