Traffic Sign Classification using Support Vector Machine and Image Segmentation

Chaiyakhan, Kedkarn; Hirunyawanakul, Anusara; Chanklan, Ratiporn; Kerdprasop, Kittisak; Kerdprasop, Nittiya

doi:10.12792/iciae2015.013

Cited by 6 publications

(2 citation statements)

References 1 publication

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“…In the second stage, they separated the traffic signal from the background using the Hough transform algorithm, which identifies shapes like circles and triangles. SVM algorithms with various kernels were used for classification [2]. In this approach, the authors designed a hierarchical architecture to enhance the recognition accuracy of traffic signs [3].…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic sign recognition using CNN and Res-Net

Cruz Antony,

Karpura Dheepan,

et al. 2024

EAI Endorsed Trans IoT

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In the realm of contemporary applications and everyday life, the significance of object recognition and classification cannot be overstated. A multitude of valuable domains, including G-lens technology, cancer prediction, Optical Character Recognition (OCR), Face Recognition, and more, heavily rely on the efficacy of image identification algorithms. Among these, Convolutional Neural Networks (CNN) have emerged as a cutting-edge technique that excels in its aptitude for feature extraction, offering pragmatic solutions to a diverse array of object recognition challenges. CNN's notable strength is underscored by its swifter execution, rendering it particularly advantageous for real-time processing. The domain of traffic sign recognition holds profound importance, especially in the development of practical applications like autonomous driving for vehicles such as Tesla, as well as in the realm of traffic surveillance. In this research endeavour, the focus was directed towards the Belgium Traffic Signs Dataset (BTS), an encompassing repository comprising a total of 62 distinct traffic signs. By employing a CNN model, a meticulously methodical approach was obtained commencing with a rigorous phase of data pre-processing. This preparatory stage was complemented by the strategic incorporation of residual blocks during model training, thereby enhancing the network's ability to glean intricate features from traffic sign images. Notably, our proposed methodology yielded a commendable accuracy rate of 94.25%, demonstrating the system's robust and proficient recognition capabilities. The distinctive prowess of our methodology shines through its substantial improvements in specific parameters compared to pre-existing techniques. Our approach thrives in terms of accuracy, capitalizing on CNN's rapid execution speed, and offering an efficient means of feature extraction. By effectively training on a diverse dataset encompassing 62 varied traffic signs, our model showcases a promising potential for real-world applications. The overarching analysis highlights the efficacy of our proposed technique, reaffirming its potency in achieving precise traffic sign recognition and positioning it as a viable solution for real-time scenarios and autonomous systems.

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Section: Literature Reviewmentioning

confidence: 99%

Traffic sign recognition using CNN and Res-Net

Cruz Antony,

Karpura Dheepan,

et al. 2024

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

show abstract

“…Robustness testing can help identify potential limitations of the algorithm and further improve its performance in real-world scenarios [9]. 2) Multi-class Traffic Sign Recognition [10]: The current algorithm focuses on binary traffic sign recognition (i.e., detecting and recognizing a single type of traffic sign) [11]. Future research can be expanded to consider multi-class traffic sign recognition, where the algorithm can accurately detect and recognize multiple types of traffic signs simultaneously, which is more representative of real-world scenarios [12].…”

Section: VImentioning

confidence: 99%

Driver Assistant Using Deep Learning

Patil¹

2023

IJRASET

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In the development of intelligent vehicles, accurate traffic sign detection and recognition are critical. This project proposes an improved algorithm for traffic sign detection and recognition, aiming to address limitations of traditional methods such as environmental sensitivity and poor real-time performance of deep learning-based approaches. The algorithm employs HSV-based spatial threshold segmentation for effective traffic sign detection based on shape features. Furthermore, the algorithm enhances the classical convolutional neural network model by utilizing Gabor kernel for initial convolution, incorporating batch normalization after pooling, and employing the Adam optimizer algorithm. The proposed algorithm is evaluated using the German Traffic Sign Recognition Benchmark, achieving a favorable prediction and accurate recognition rate of 99.75%, with an average processing time of 5.4ms per frame. Compared to other algorithms, the proposed approach demonstrates superior accuracy, real-time performance, generalization ability, and training efficiency. The findings of this project are expected to contribute to reducing accident rates and enhancing road traffic safety through improved traffic sign recognition in intelligent vehicles.

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Lane Sensing and Tracing Algorithms for Advanced Driver Assistance Systems with Object Detection and Traffic Sign Recognition

Machaiah

Pavithra

2023

Lecture Notes in Networks and Systems

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Traffic Sign Classification using Support Vector Machine and Image Segmentation

Cited by 6 publications

References 1 publication

Traffic sign recognition using CNN and Res-Net

Traffic sign recognition using CNN and Res-Net

Driver Assistant Using Deep Learning

Lane Sensing and Tracing Algorithms for Advanced Driver Assistance Systems with Object Detection and Traffic Sign Recognition

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