Traffic sign detection and recognition using fuzzy segmentation approach and artificial neural network classifier respectively

• Published in: 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE) pp. 518 - 523
• Main Authors: Abedin, Zainal, Dhar, Prashengit, Hossenand, Muhammad Kamal, Deb, Kaushik
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.02.2017
• Subjects: Artificial Neural Network; Artificial neural networks; Confusion matrix; Cross Entropy; Feature extraction; Filtering; Fuzzy Rules; Image color analysis; Image segmentation; Receiver Operating characteristic Curve; Roads; Robustness; Speeded Up Robust Feature; Traffic Sign Recognition
• DOI: 10.1109/ECACE.2017.7912960

Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. This paper represents a new approach for TSR system where detection of traffic sign is carried out using fuzzy rules based color segmentation method and recognition is accomplished using Speeded Up Robust Features(SURF) descriptor, trained by artificial neural network (ANN) classifier. In the detection step, the region of interest (sign area) is segmented using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space, post processed to filter unwanted region. Finally the recognition of the traffic sign is implemented using ANN classifier upon the training of SURF features descriptor. The proposed system simulated on offline road scene images captured under different illumination conditions. The detection algorithm shows a high robustness and the recognition rate is quite satisfactory. The performance of the ANN model is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curves. Also, performances of some classifier such as Support Vector Machine (SVM), Decision Trees, Ensembles Learners (Adaboost) and K-Nearest Neighbor (KNN) classifier are assessed with ANN approach. The simulation results illustrate that recognition using ANN model is higher than classifiers stated above.