MDSF-YOLO: Advancing Object Detection With a Multiscale Dilated Sequence Fusion Network.

Abstract

Accurate and fast detection of traffic signs is critical for autonomous driving, particularly in complex environments with diverse sign scales and varying detection distances. Existing approaches, incorporating attention modules or modifying detection heads, frequently encounter high rates of false positives and omissions due to the increased sampling depth. To address these limitations, we propose MDSF-you only look once (YOLO), a novel detection framework that integrates multiscale sequence fusion (MSF) for synergistic feature integration across granularities, enhancing the precision of both localization and semantic information fusion. Additionally, our dilated-wise residual (DWR) module leverages dilated convolutions and channel-wise reparameterization to improve fine-grained feature extraction. The architecture further introduces a $P_{2}$ detection head for shallow features and fully decouples all detection heads, optimizing target localization and category identification. Extensive experiments on the TT100K and CCTSDB2021 datasets demonstrate the superiority of MDSF-YOLO over benchmark models, including YOLOv11s, with significant improvements in mAP by 8.8% and 2.4% on respective datasets while substantially reducing false positives and leakage rate. Besides, the marked improvement of MDSF-YOLO on the VisDrone2019 dataset verifies its enhanced capability to address drone-based object detection. These advances underscore the efficiency and robustness of the proposed model, providing a promising solution for autonomous driving and similar object detection scenarios.