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Traffic Sign Recognition System Using Convolutional Neural NetworkDevineni
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<div class="card"> <div class="card-body"><strong>Paper Count:</strong> 87358</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Traffic Sign Recognition System Using Convolutional Neural NetworkDevineni</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devineni%20Vijay%20Bhaskar">Devineni Vijay Bhaskar</a>, <a href="https://publications.waset.org/abstracts/search?q=Yendluri%20Raja"> Yendluri Raja</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”. <iframe src="https://publications.waset.org/abstracts/149896.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20signs" title=" traffic signs"> traffic signs</a>, <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20procedures" title=" bootstrap procedures"> bootstrap procedures</a>, <a href="https://publications.waset.org/abstracts/search?q=precision-recall%20curve" title=" precision-recall curve"> precision-recall curve</a> </p> <a href="https://publications.waset.org/abstracts/149896/traffic-sign-recognition-system-using-convolutional-neural-networkdevineni" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149896.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a 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