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- W4360982719 abstract "These days, traffic signs etched on the roadways improve traffic safety by notifying drivers of speed restrictions as well as other potential hazards such as deep curvy routes, upcoming road repairs, or pedestrian crossings. This study focuses on a three-stage real-time Traffic Sign Recognition and Classification system that includes image segmentation, traffic sign detection, and classification based on the input image. To extract red areas from an image, a colour enhancement approach is utilized. Convolutional Neural Networks (CNN) are used to determine the content of the traffic signals discovered during detection, classification, and identification. Results show that CNN model provides the highest accuracy of 99% in validation phase." @default.
- W4360982719 created "2023-03-30" @default.
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- W4360982719 date "2023-01-01" @default.
- W4360982719 modified "2023-09-27" @default.
- W4360982719 title "Convolutional Neural Networks for Traffic Sign Classification Using Enhanced Colours" @default.
- W4360982719 doi "https://doi.org/10.1007/978-3-031-27622-4_4" @default.
- W4360982719 hasPublicationYear "2023" @default.
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