DSF-YOLO for robust multiscale traffic sign detection under adverse weather conditions

With the rapid development of autonomous driving technology, traffic sign recognition (TSR) has emerged as a foundational component of mobile driving systems. Although significant progress has been made in current research, existing techniques still face challenges in recognizing traffic signs under complex weather conditions. This model employs an attention-based dynamic sequence fusion feature pyramid, which enhances recognition accuracy for small-target traffic sign instances in adverse weather, as opposed to traditional feature pyramid networks. Additionally, the model integrates a dynamic snake convolution operator along with Wise-IoU, enabling it to capture fine small-scale feature information while mitigating the impact of low-quality instances. Furthermore, the model introduces a novel data augmentation library, Albumentations, to simulate real-world complex weather scenarios, and utilizes a new performance evaluation metric, TIDE, to more effectively assess model performance in such conditions. We demonstrate the effectiveness of our model on the TT-100 K dataset, the GTSDB dataset, and the BDD 100 K dataset, achieving improvements in mAP of 9%, 1.5%, and 2.6%, respectively. Compared to the baseline model, Cls and Loc metrics decreased by approximately 3 and 1.2.The experiments indicate that our model exhibits excellent generalization ability and robustness, successfully performing small target detection under complex weather conditions in the realm of traffic sign recognition.