CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions
This addresses a critical safety problem for autonomous driving systems by enhancing detection in adverse weather, though it is incremental as it builds on existing detection and denoising techniques.
The paper tackled traffic sign detection under extreme conditions like fog and rain by proposing CCSPNet-Joint, a joint training model that improved precision by 5.32% and mAP@.5 by 18.09% compared to end-to-end methods.
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 mAP@.5.