Traffic signs recognition (TSR) plays an essential role in assistant driving and intelligent transportation system. However, the noise of complex environment may lead to motion-blur or occlusion problems, which raise the tough challenge to real-time recognition with high accuracy and robust. In this article, we propose IECES-network which with improved encoders and Siamese net. The three-stage approach of our method includes Efficient-CNN based encoders, Siamese backbone and the fully-connected layers. We firstly use convolutional encoders to extract and encode the traffic sign features of augmented training samples and standard images. Then, we design the Siamese neural network with Efficient-CNN based encoder and contrastive loss function, which can be trained to improve the robustness of TSR problem when facing the samples of motion-blur and occlusion by computing the distance between inputs and templates. Additionally, the template branch of the proposed network can be stopped when executing the recognition tasks after training to raise the process speed of our real-time model, and alleviate the computational resource and parameter scale. Finally, we recombined the feature code and a fully-connected layer with SoftMax function to classify the codes of samples and recognize the category of traffic signs. The results of experiments on the Tsinghua-Tencent 100K dataset and the German Traffic Sign Recognition Benchmark dataset demonstrate the performance of the proposed IECESnetwork. Compared with other state-of-the-art methods, in the case of motion-blur and occluded environment, the proposed method achieves competitive performance precision-recall and accuracy metric average is 88.1%, 86.43% and 86.1% with a 2.9M lightweight scale, respectively. Moreover, processing time of our model is 0.1s per frame, of which the speed is increased by 1.5 times compared with existing methods.

翻译：交通标志识别在辅助驾驶和智能交通系统中具有重要作用。然而，复杂环境中的噪声可能导致运动模糊或遮挡问题，这对实现高精度、强鲁棒性的实时识别提出了严峻挑战。本文提出了一种改进编码器的孪生网络IECES-network。该方法采用三阶段架构：基于高效CNN的编码器、孪生网络主干和全连接层。我们首先使用卷积编码器对增强训练样本和标准图像的交通标志特征进行提取与编码。随后，设计了基于高效CNN编码器的孪生神经网络，通过对比损失函数计算输入样本与模板之间的距离，从而提升模型在面对运动模糊和遮挡样本时的鲁棒性。此外，在完成训练后的识别任务中可停止模板分支的计算，以提高实时模型的处理速度，同时减少计算资源和参数量。最后，通过重组特征编码并采用带SoftMax函数的全连接层，对样本编码进行分类以实现交通标志类别识别。在清华-腾讯100K数据集和德国交通标志识别基准数据集上的实验结果表明，所提出的IECES-network具有优异性能。与其他先进方法相比，在运动模糊和遮挡环境下，该方法以290万参数量实现了88.1%的精确率-召回率综合指标和86.43%的平均准确率，处理速度达到每帧0.1秒，较现有方法提升1.5倍。