Traffic sign detection is an important research direction in intelligent driving. Unfortunately, existing methods often overlook extreme conditions such as fog, rain, and motion blur. Moreover, the end-to-end training strategy for image denoising and object detection models fails to utilize inter-model information effectively. To address these issues, we propose CCSPNet, an efficient feature extraction module based on Transformers and CNNs, which effectively leverages contextual information, achieves faster inference speed and provides stronger feature enhancement capabilities. Furthermore, we establish the correlation between object detection and image denoising tasks and propose a joint training model, CCSPNet-Joint, to improve data efficiency and generalization. Finally, to validate our approach, we create the CCTSDB-AUG dataset for traffic sign detection in extreme scenarios. Extensive experiments have shown that CCSPNet achieves state-of-the-art performance in traffic sign detection under extreme conditions. Compared to end-to-end methods, CCSPNet-Joint achieves a 5.32% improvement in precision and an 18.09% improvement in [email protected].

翻译：交通标志检测是智能驾驶中的重要研究方向。然而，现有方法常常忽略雾、雨和运动模糊等极端条件。此外，针对图像去噪和目标检测模型的端到端训练策略未能有效利用模型间的信息。为解决这些问题，我们提出了一种基于Transformer和CNN的高效特征提取模块CCSPNet，该模块能有效利用上下文信息、实现更快的推理速度并提供更强的特征增强能力。进一步地，我们建立了目标检测与图像去噪任务之间的关联性，并提出了一种联合训练模型CCSPNet-Joint，以提高数据效率和泛化能力。最后，为验证所提方法，我们创建了面向极端场景下交通标志检测的CCTSDB-AUG数据集。大量实验表明，CCSPNet在极端条件下交通标志检测中达到了最先进的性能。与端到端方法相比，CCSPNet-Joint在精确率上提升了5.32%，在[email protected]指标上提升了18.09%。