Affiliations: [a]
Department of Electronics & Communication Engineering, Saveetha Engineering College, Thandalam, Tamilnadu, India
| [b]
Department of Electronics and Communication Engineering, Sir Isaac Newton College of Engineering and Technology, Nagapattinam, Tamilnadu, India
| [c]
Department of Electronics and Communication Engineering, Kings Engineering College, Irungattukottai, Chennai | [d]
Department of Electronics and Communication Engineering, DVR & Dr. HS MIC College of Technology, Kanchikacherla
Correspondence:
[*]
Corresponding author. Raji Pandurangan, Department of Electronics & Communication Engineering, Saveetha Engineering College, Thandalam, Tamilnadu, India. rajipandurangan@saveetha.ac.in.
Abstract: The traffic signal recognition model plays a significant role in the intelligent transportation model, as traffic signals aid the drivers to driving the more professional with awareness. The primary goal of this paper is to proposea model that works for the recognition and detection of traffic signals. This work proposes the pre-processing and segmentation approach applying machine learning techniques are occurred recent trends of study. Initially, the median filter & histogram equalization technique is utilized for pre-processing the traffic signal images, and also information of the figures being increased. The contrast of the figures upgraded, and information about the color shape of traffic signals are applied by the model. To localize the traffic signal in the obtained image, then this region of interest in traffic signal figures are extracted. The traffic signal recognition and classification experiments are managed depending on the German Traffic Signal Recognition Benchmark-(GTSRB). Various machine learning techniques such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Convolutional neural network (CNN)- General Regression Neural Network (GRNN) is used for the classification process. Finally, the obtained results will be compare in terms of the performance metrics like accuracy, F1 score, kappa score, jaccard score, sensitivity, specificity, recall, and precision. The result shows that CNN-GRNN with ML techniques by attaining 99.41% accuracy compare to other intelligent methods. In this proposed technique is used for detecting and classifying various categories of traffic signals to improve the accuracy and effectiveness of the system.
Keywords: Traffic signal images, traffic signs, median filter, gabor filter, forecasting
