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- W2913657597 abstract "Deep learning has become a ubiquitous method in object detection among multiple domains recently. However, in the era of edge computing, deploying deep neural networks on mobile edge platforms are challenging due to long latency and huge computational cost. As previous research efforts were usually focused on accuracy, achieving the balance between computational consumption and accuracy is a more significant problem to be tackled in mobile edge computing domain. To this end, we proposed an efficient convolutional neural network (CNN), which can remarkably minimize the redundancy, reduce the parameters and speed up the networks. The effectiveness of the network is further proved with experiments on a Tsinghua-Tencent 100K traffic sign dataset. Results show that under the same-level model size, our network outperforms the state-of-the-art Fast R-CNN and Faster R-CNN with 10% improvement in accuracy. Compared to similar work, the computational consumption on running time and memory of our network has been also reduced in the premise of little loss in accuracy." @default.
- W2913657597 created "2019-02-21" @default.
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- W2913657597 date "2019-08-01" @default.
- W2913657597 modified "2023-10-13" @default.
- W2913657597 title "An efficient convolutional neural network for small traffic sign detection" @default.
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- W2913657597 doi "https://doi.org/10.1016/j.sysarc.2019.01.012" @default.
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