Traffic sign detection is a challenging task. Although existing deep learning techniques have made great progress in detecting traffic signs, there are still many unsolved challenges. We propose a novel traffic sign detection network named ReYOLO that learns rich contextual information and senses scale variations to efficiently detect small and ambiguous traffic signs in the wild. Specifically, we first replace the conventional convolutional block with modules that are built by structural reparameterization methods and are embedded into bigger structures, thus decoupling the training structures and the inference structures using parameter transformation, and allowing the model to learn more effective features. We then design a novel weighting mechanism which can be embedded into a feature pyramid to exploit foreground features at different scales to narrow the semantic gap between multiple scales. To fully evaluate the proposed method, we conduct experiments on a traditional traffic sign dataset GTSDB as well as two new traffic sign datasets TT100K and CCTSDB2021, achieving 97.2%, 68.3% and 83.9% mAP (Mean Average Precision) for the three-class detection challenge in these three datasets.

中文翻译：

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ReYOLO：一种基于网络重新参数化和自适应加权的交通标志检测器

交通标志检测是一项具有挑战性的任务。尽管现有的深度学习技术在检测交通标志方面取得了长足的进步，但仍有许多未解决的挑战。我们提出了一种名为 ReYOLO 的新型交通标志检测网络，它可以学习丰富的上下文信息并感知尺度变化，以有效地检测野外的小而模糊的交通标志。具体来说，我们首先将传统的卷积块替换为通过结构重新参数化方法构建并嵌入到更大结构中的模块，从而通过参数变换将训练结构和推理结构解耦，让模型学习更有效的特征。然后，我们设计了一种新颖的加权机制，可以嵌入到特征金字塔中，以利用不同尺度的前景特征来缩小多个尺度之间的语义差距。为了充分评估所提出的方法，我们在一个传统的交通标志数据集 GTSDB 以及两个新的交通标志数据集 TT100K 和 CCTSDB2021 上进行了实验，三类检测的 mAP（平均精度）分别达到了 97.2%、68.3% 和 83.9%这三个数据集中的挑战。