A collection of benchmark images for traffic sign research

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

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Research on Color Normalization of Traffic Sign Image Based on Surface Segmentation and Improved K-means Clustering Algorithm

Proceedings of the 4th International Conference on Computer Science and Application Engineering ◽

10.1145/3424978.3425155 ◽

2020 ◽

Author(s):

Yang Liu ◽

Wei Zhong

Keyword(s):

Clustering Algorithm ◽

Traffic Sign ◽

Surface Segmentation

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Thai Traffic Sign Classification and Recognition System Based on Histogram of Gradients, Color Layout Descriptor, and Normalized Correlation Coefficient

2020 - 5th International Conference on Information Technology (InCIT) ◽

10.1109/incit50588.2020.9310778 ◽

2020 ◽

Author(s):

Nattapol Namyang ◽

Suphakant Phimoltares

Keyword(s):

Correlation Coefficient ◽

Recognition System ◽

Traffic Sign ◽

Histogram Of Gradients ◽

Traffic Sign Classification

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Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s089006042100010x ◽

2021 ◽

Vol 35 (2) ◽

pp. 251-264

Author(s):

Dimitrios Boursinos ◽

Xenofon Koutsoukos

Keyword(s):

Neural Networks ◽

Distance Learning ◽

Real Time ◽

Speaker Recognition ◽

Deep Neural Networks ◽

Cyber Physical Systems ◽

Error Rates ◽

Traffic Sign ◽

Conformal Prediction ◽

Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

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