A novel hybrid machine learning approach for traffic sign detection using CNN-GRNN

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.

Development of Deep Learning Models for Traffic Sign Recognition in Autonomous Vehicles

“…One of the key insights gleaned from this research is the significant impact of CNN architecture on the performance of traffic sign recognition models. As demonstrated in previous studies [33], the hierarchical feature extraction capabilities of CNNs enable the automatic learning of discriminative features from raw image data, thereby facilitating accurate classification of traffic signs [34]. By leveraging multiple convolutional layers followed by fusion, flatten, dropout, and dense layers, the proposed CNN architecture effectively captures spatial dependencies and semantic information inherent in traffic sign images, leading to superior recognition performance.…”

Trend analysis of traffic management based on literature data mining and graph analysis tools

“…In addition, machine learning methods are also used in predicting traffic crash severity [23], classifying traffic conditions [130], and detecting traffic sign [131]. However, the commonly-used models are different in different research fields, such as Artificial Intelligence method in ITS field [132] and Artificial neural network approach in predicting road traffic accidents [133].…”

“…performed the bibliometric analysis on traffic flow prediction and found that the USA and China are the countries with most works published on socially sustainable transport [16], Autonomous Vehicles [129], or subgrade stability and reinforcement with special soil [25]. In addition, machine learning methods are also used in predicting traffic crash severity [23], classifying traffic conditions [130], and detecting traffic sign [131]. However, the commonly‐used models are different in different research fields, such as Artificial Intelligence method in ITS field [132] and Artificial neural network approach in predicting road traffic accidents [133].…”