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

Yu Sun , Chong Zhang , Xian Li

IEEE Transactions on Neural Networks and Learning Systems +

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October 7, 2025

View abstract on PubMed

Summary

This study introduces MDSF-YOLO, a new traffic sign detection system for autonomous driving. It significantly reduces errors and improves accuracy in complex environments.

Area Of Science

Computer Vision
Machine Learning
Autonomous Systems

Background

Accurate traffic sign detection is crucial for autonomous driving safety.
Existing methods struggle with varied sign scales, distances, and complex environments, leading to false positives and omissions.
Increased sampling depth in current models exacerbates detection challenges.

Purpose Of The Study

To develop a novel traffic sign detection framework, MDSF-YOLO, to overcome limitations of existing approaches.
To enhance the precision of both localization and semantic information fusion in traffic sign recognition.
To improve fine-grained feature extraction and optimize target localization and category identification.

Main Methods

Integration of multiscale sequence fusion (MSF) for synergistic feature integration across granularities.
Introduction of a dilated-wise residual (DWR) module using dilated convolutions and channel-wise reparameterization.
Implementation of a P2 detection head for shallow features and fully decoupled detection heads.

Main Results

MDSF-YOLO demonstrated superior performance over benchmark models on TT100K and CCTSDB2021 datasets, with mAP improvements of 8.8% and 2.4% respectively.
The model significantly reduced false positives and leakage rates.
Enhanced capabilities were verified on the VisDrone2019 dataset for drone-based object detection.

Conclusions

MDSF-YOLO offers an efficient and robust solution for traffic sign detection in autonomous driving.
The proposed framework effectively addresses challenges posed by diverse sign scales and detection distances.
The model provides a promising advancement for real-world autonomous driving applications and similar object detection tasks.