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Traffic-Sign Detection and Classification in the Wild

CVPR 2016 · Detection and segmentation › CNN features · approach: Pedestrian and crowd detection · 1,007 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the fully convolutional network for simultaneous sign detection and classification instead of Fast R-CNNTraffic-sign recognition · Tsinghua-Tencent 100K testaccuracy: 0.50 -> 0.88Helped
Use the fully convolutional network for simultaneous sign detection and classification instead of Fast R-CNNTraffic-sign recognition · Tsinghua-Tencent 100K testrecall: 0.56 -> 0.91Helped
Use the fully convolutional network instead of Fast R-CNNTraffic-sign recognition · Tsinghua-Tencent 100K, sign size (0,32]accuracy: 0.45 -> 0.82Helped
Use the fully convolutional network instead of Fast R-CNNTraffic-sign recognition · Tsinghua-Tencent 100K, sign size (0,32]recall: 0.24 -> 0.87Helped
Use the fully convolutional network instead of Fast R-CNNTraffic-sign recognition · Tsinghua-Tencent 100K, sign size (32,96]accuracy: 0.51 -> 0.91Helped
Use the fully convolutional network instead of Fast R-CNNTraffic-sign recognition · Tsinghua-Tencent 100K, sign size (32,96]recall: 0.74 -> 0.94Helped
Use the fully convolutional network instead of Fast R-CNNTraffic-sign recognition · Tsinghua-Tencent 100K, sign size (96,400]accuracy: 0.55 -> 0.91Helped
Use the fully convolutional network instead of Fast R-CNNTraffic-sign recognition · Tsinghua-Tencent 100K, sign size (96,400]recall: 0.86 -> 0.88Helped

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