Convolutional neural networks (CNNs) have achieved significant progress in computer vision systems, helping to efficiently obtain feature information by sliding filters on the input images. However, CNNs have difficulty capturing specific properties when the images are affected by various noises. This paper proposes an attention based convolutional pooling neural network (ACPNN) where an attention-mechanism is applied to feature maps to obtain key features, and max pooling is replaced with convolutional pooling to improve recognition accuracy in harsh environments. The ACPNN with attention mechanism and convolutional pooling structure is robust against external noises and maintains classification performance under such conditions. The proposed ACPNN was validated on the German traffic sign recognition benchmark with various cases. Considering the traffic signs are suffered from various noises, the recognition performances were demonstrated with conventional CNN and state-of-the art CNNs such as multi-scale CNN, committee of CNN, hierarchical CNN, and multi-column deep neural network. Under such harsh conditions, the proposed ACPNN shows 66.981% and 83.198% respectively, which are the best performances compared to other CNNs.

中文翻译：

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基于注意力的卷积池神经网络在恶劣环境下的交通标志识别

卷积神经网络（CNN）在计算机视觉系统中取得了重大进展，通过在输入图像上滑动过滤器来帮助有效地获取特征信息。但是，当图像受到各种噪声影响时，CNN难以捕获特定属性。本文提出了一种基于注意力的卷积池神经网络（ACPNN），其中将注意力机制应用于特征图以获得关键特征，并用卷积池代替最大池以提高在恶劣环境下的识别精度。具有注意机制和卷积池结构的ACPNN可以抵抗外部噪声，并在这种情况下保持分类性能。拟议的ACPNN已在各种情况下在德国交通标志识别基准上得到验证。考虑到交通标志受到各种噪声的影响，使用常规的CNN和最新的CNN（例如多尺度CNN，CNN委员会，分层CNN和多列深度神经网络）展示了识别性能。在这种恶劣条件下，拟议的ACPNN分别显示66.981％和83.198％，这是与其他CNN相比最佳的性能。