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 mAP@.5.

提出了一种基于Transformer和CNN的高效特征提取模块CCSPNet，它有效地利用上下文信息，实现更快的推理速度并提供更强的特征增强能力，与图像去噪和目标检测任务建立了相关性，并提出了联合训练模型CCSPNet-Joint来提高数据效率和泛化能力。通过创建CCTSDB-AUG数据集验证了这种方法，在极端条件下的交通标志检测方面，CCSPNet取得了最先进的性能，相比于端到端方法，CCSPNet-Joint在精度上提升了5.32%，在mAP@.5上提升了18.09%。

CCSPNet-Joint: 极端条件下交通违章检测的高效联合训练方法