EFFE-YOLO: An Improved Algorithm for Small-Target Traffic Sign Detection.

Saved in:

Bibliographic Details
Title:	EFFE-YOLO: An Improved Algorithm for Small-Target Traffic Sign Detection.
Authors:	Lu, Guangyao¹ 18838927982@163.com, Liu, Weisheng² succman@163.com
Source:	Engineering Letters. Jun2026, Vol. 34 Issue 6, p2336-2349. 14p.
Subjects:	Traffic signs & signals, Object recognition (Computer vision), Computer vision
Abstract:	Extreme scale variation and severe occlusion in complex driving environments significantly degrade the detection performance of small traffic signs. To address these challenges, this paper presents EFFE-YOLO, an accurate detection model based on YOLOv11n. Specifically, the Cross-Stage Partial Network and Parallel Multi-Scale Feature Fusion Attention (CSP-PMSFA) module enhances deep multi-scale semantic feature extraction, and the Cross-scale Alignment Zone Neck (CAZ Neck) achieves seamless cross-scale feature alignment. The Adaptive Downsampling (ADown) algorithm reduces information loss and preserves fine-grained details critical for small targets. Furthermore, the integration of Scale Sequence Feature Fusion (ScalSeq) and the P2 detection layer effectively fuses multi-scale feature maps, retains high-resolution information for small targets, and strengthens the model's multi-scale detection ability. We propose an Inner-GIoU loss function equipped with auxiliary bounding boxes and a scaling factor mechanism to improve bounding box regression accuracy. Experimental results show that EFFE-YOLO achieves 77.99% mAP@50 and 60.24% mAP@50:95 on TT100K, with improvements of 1.82% mAP@50 and 3.07% mAP@50:95 on CCTSDB over the baseline model. Specifically, it yields a 24.98% mAP@50 improvement for small targets on TT100K, validating its superiority in small-scale traffic sign detection. [ABSTRACT FROM AUTHOR]
	Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	Extreme scale variation and severe occlusion in complex driving environments significantly degrade the detection performance of small traffic signs. To address these challenges, this paper presents EFFE-YOLO, an accurate detection model based on YOLOv11n. Specifically, the Cross-Stage Partial Network and Parallel Multi-Scale Feature Fusion Attention (CSP-PMSFA) module enhances deep multi-scale semantic feature extraction, and the Cross-scale Alignment Zone Neck (CAZ Neck) achieves seamless cross-scale feature alignment. The Adaptive Downsampling (ADown) algorithm reduces information loss and preserves fine-grained details critical for small targets. Furthermore, the integration of Scale Sequence Feature Fusion (ScalSeq) and the P2 detection layer effectively fuses multi-scale feature maps, retains high-resolution information for small targets, and strengthens the model's multi-scale detection ability. We propose an Inner-GIoU loss function equipped with auxiliary bounding boxes and a scaling factor mechanism to improve bounding box regression accuracy. Experimental results show that EFFE-YOLO achieves 77.99% mAP@50 and 60.24% mAP@50:95 on TT100K, with improvements of 1.82% mAP@50 and 3.07% mAP@50:95 on CCTSDB over the baseline model. Specifically, it yields a 24.98% mAP@50 improvement for small targets on TT100K, validating its superiority in small-scale traffic sign detection. [ABSTRACT FROM AUTHOR]
ISSN:	1816093X