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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.

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Identifying and separating objects within an image is a significant challenge due to high object and background variability. This can be obtained through feature extraction approach. There are different ways of extracting the image features. It is based on texture, shape and colour. The present paper aims to study and analyse the various approaches for feature extraction and object recognition. This study proposed a hybrid approach, which is a combination of enhanced Fractal Texture Analysis with Layout Descriptor to overcome the obstacles in image segmentation. It is used to lessen the boundary complexity of the segmented image. First, the image is preprocessed to discard the noise and to retain the adequate details of the image in a perfect way through Adaptive Switching Median Filter. Secondly, it improves the power of the edges detected through a noise-protected edge detector. Finally, it is applied with morphological gradient technique that is a twin function of both shape and texture gradient removal for extorting the qualities of the image. In this way, the proposed methodology directly performs on the colour image which supports to enhance prediction accuracy of the object in terms of colour characteristics that offers better results than the grayscale conversion approach. Moreover, the shape feature is extracted from the preprocessed image depending on the details like compactness, rectangularity, eccentricity and moment invariants.