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Traffic light recognition using deep neural networks

机译:使用深度神经网络的交通灯识别

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Conventional traffic light detection methods often suffers from false positives in urban environment because of the complex backgrounds. To overcome such limitation, this paper proposes a method that combines a conventional approach, which is fast but weak to false positives, and a DNN, which is not suitable for detecting small objects but a very powerful classifier. Experiments on real data showed promising results.
机译:由于背景复杂,传统的交通信号灯检测方法在城市环境中经常遭受误报。为了克服这种局限性,本文提出了一种方法,该方法结合了快速但对误报较弱的常规方法和不适合检测小物体但功能强大的分类器的DNN。对真实数据的实验显示出令人鼓舞的结果。

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