  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 Contextual Transformer and CNN,
capable of effectively utilizing the static and dynamic features of images,
achieving faster inference speed and providing 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_ADDRESS>