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• W3202671457 abstract "Whenever it comes to road safety, traffic signs plays a vital role. Understanding and following different categories of traffic sign is also important. Several methods are available to tackle this problem of traffic sign detection and one of the method is the object detection algorithm. Generic object detectors such as R-CNN, Fast RCNN, Faster RCNN, YOLO, etc. can perform multiple detection in a frame. Generic object detectors fails in identifying small objects with in a frame due to the usage of pooling with the convolutional layer. Pooling layer, filters out the low level features, but this cannot happen when we deal with real time cases. Because in real scenarios the traffic sign contributes only a small portion of the image. TT100K dataset is the traffic sign dataset that can be used to train proposed model as it meets the needs of our problem. Here in this paper we try to overcome the issue of low level features by employing ridgelet filters to get the enhanced features." @default.
• W3202671457 created "2021-10-11" @default.
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• W3202671457 date "2021-08-04" @default.
• W3202671457 modified "2023-09-24" @default.
• W3202671457 title "Enhanced Local Features using Ridgelet Filters for Traffic Sign Detection and Recognition" @default.
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• W3202671457 doi "https://doi.org/10.1109/icesc51422.2021.9532967" @default.
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