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- W4254158334 abstract "In this chapter, the authors report a system for detection and classification of road signs. This system consists of two parts. The first part detects the road signs in real time. The second part classifies the German traffic signs (GTSRB) dataset and makes the prediction using the road signs detected in the first part to test the effectiveness. The authors used HOG and SVM in the detection part to detect the road signs captured by the camera. Then they used a convolutional neural network based on the LeNet model in which some modifications were added in the classification part. The system obtains an accuracy rate of 96.85% in the detection part and 96.23% in the classification part." @default.
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- W4254158334 date "2020-01-01" @default.
- W4254158334 modified "2023-10-17" @default.
- W4254158334 title "A Robust System for Road Sign Detection and Classification Using LeNet Architecture Based on Convolutional Neural Network" @default.
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- W4254158334 doi "https://doi.org/10.4018/978-1-7998-4444-0.ch004" @default.
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