The fact that the existing traffic sign images are easily affected by external factors, and the traffic signs are generally small targets on the images at different scales, has made it difficult in feature extraction when doing traffic sign detection. To achieve better detection results, a multi-target traffic sign detection method with channel attention and feature fusion network (CAFFNet in short) is proposed. This method effectively learns the correlation between feature channels through a lightweight channel attention network, realizes local cross-channel interaction without dimensionality reduction, and enhances the representation ability of the network. The feature pyramid network is used to achieve feature fusion and generate high-resolution multiscale semantic information. The dilated convolution is utilized to capture the multiscale context information to narrow the difference between features and improve the detection effect of the model. The experimental results show that the proposed method on the two datasets GTSDB and CTSD has achieved superior performance in the evaluation criteria compared with the existing detection algorithms.

中文翻译：

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CAFFNet：用于多目标交通标志检测的通道注意和特征融合网络

现有交通标志图像易受外界因素影响，交通标志一般是不同尺度图像上的小目标，使得在进行交通标志检测时特征提取困难。为了获得更好的检测效果，提出了一种通道注意力和特征融合网络（简称CAFFNet）的多目标交通标志检测方法。该方法通过轻量级的通道注意力网络有效学习特征通道之间的相关性，在不降维的情况下实现局部跨通道交互，增强网络的表示能力。特征金字塔网络用于实现特征融合，生成高分辨率多尺度语义信息。空洞卷积用于捕获多尺度上下文信息，以缩小特征之间的差异，提高模型的检测效果。实验结果表明，与现有检测算法相比，所提出的方法在 GTSDB 和 CTSD 两个数据集上的评价标准均取得了优越的性能。