提出了一种基于改进卷积神经网络的交通标志检测方法。预训练模型产生否定;使用难分类负样本采集将负样本输入到网络中,提高模型的判别能力;使用多尺度训练过程中的特征级联策略来进一步提升模型的性能。利用TensorFlow框架在德国交通标志检测数据集上对所提方法的有效性进行了仿真。研究结果表明,与现有技术相比,所提方法能够获得更快的检测速率,处理每幅图像仅需0.016 s。

A detection method of traffic signs is proposed based on a modified convolutional neural network. The model is pre-trained to produce the negatives, and hard negative mining is used to add the negative samples into the network to improve the discriminating ability of the model. A feature concatenation strategy during the multi-scale training process is employed to further enhance the performance of the model. On the German traffic sign detection dataset, the effectiveness of the proposed method is simulated in the TensorFlow framework. The research results show that compared with the existing methods, the proposed method can be used to obtain a high detection rate and processing time of only 0.016 s for each image.