MFSF-YOLO: An Improved YOLO11 Model for Traffic Sign Detection and Recognition.

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Bibliographic Details
Title:	MFSF-YOLO: An Improved YOLO11 Model for Traffic Sign Detection and Recognition.
Authors:	Ma, Simin¹ 1820077692@qq.com, Zhao, Nannan² 723306003@qq.com, Li, Jiangwei¹ 2369156940@qq.com, Ouyang, Xinyu¹ 13392862@qq.com
Source:	Engineering Letters. Jun2026, Vol. 34 Issue 6, p2225-2235. 11p.
Subjects:	Feature extraction, Object recognition (Computer vision), Cost functions, Computer vision, Traffic signs & signals, Signal detection
Abstract:	Aiming at the problems of high false detection rate, high missed detection rate, low precision, and poor robustness in traffic target detection within computer vision tasks, a model called MFSF-YOLO11 (YOLO11 with Multi-directional information Flow and Scale-adaptive Fusion) is proposed. First, an improved feature extraction method is proposed. By designing the SPPCAKO module to replace the original SPPF module, the capability and accuracy of feature extraction are effectively enhanced. In terms of feature fusion, an innovative SDI-Damo Neck structure is put forward, which significantly improves the detection accuracy and robustness of YOLO series models in practical applications. To further boost the detection performance, a brand-new OASFFHead structure is introduced in the detection head part. This structure can simultaneously take into account the directionality and scale variation of targets, thereby optimizing the overall performance of target detection. Finally, the InnerMPDIoU loss function is adopted to replace the traditional CIoU loss function, which not only improves the accuracy of target localization but also remarkably optimizes the fitting effect of detection boxes. Experimental results show that the improved YOLO11 model achieves a 4.8% increase in mAP50, an 8.2% improvement in precision, and a 15.8% rise in recall on the TT100K dataset, demonstrating higher accuracy and robustness in complex environments. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Aiming at the problems of high false detection rate, high missed detection rate, low precision, and poor robustness in traffic target detection within computer vision tasks, a model called MFSF-YOLO11 (YOLO11 with Multi-directional information Flow and Scale-adaptive Fusion) is proposed. First, an improved feature extraction method is proposed. By designing the SPPCAKO module to replace the original SPPF module, the capability and accuracy of feature extraction are effectively enhanced. In terms of feature fusion, an innovative SDI-Damo Neck structure is put forward, which significantly improves the detection accuracy and robustness of YOLO series models in practical applications. To further boost the detection performance, a brand-new OASFFHead structure is introduced in the detection head part. This structure can simultaneously take into account the directionality and scale variation of targets, thereby optimizing the overall performance of target detection. Finally, the InnerMPDIoU loss function is adopted to replace the traditional CIoU loss function, which not only improves the accuracy of target localization but also remarkably optimizes the fitting effect of detection boxes. Experimental results show that the improved YOLO11 model achieves a 4.8% increase in mAP50, an 8.2% improvement in precision, and a 15.8% rise in recall on the TT100K dataset, demonstrating higher accuracy and robustness in complex environments. [ABSTRACT FROM AUTHOR]
ISSN:	1816093X