YOLOv3-A：基于注意力机制的交通标志检测网络

doi:10.11959/j.issn.1000-436x.2021031

Abstract

Abstract:

To solve the problem that the existing YOLOv3 algorithm had more false detections and missed detections for traffic sign detection task with small target problems and complex background, based on the YOLOv3, a channel attention method for target detection and a spatial attention method based on semantic segmentation guidance were proposed to form the YOLOv3-A (attention) algorithm.The detection features in the channel and spatial dimensions were recalibrated, allowing the network to focus and enhance the effective features, and suppress interference features, which greatly improved the detection performance.Experiments on the TT100K traffic sign data set show that the algorithm improves the detection performance of small targets, and the accuracy and recall rate of the YOLOv3 are improved by 1.9% and 2.8% respectively.

Key words: traffic sign detection, small target detection, attention mechanism, semantic segmentation

CLC Number:

TP391.41

Fan GUO, Yongxiang ZHANG, Jin TANG, Weiqing LI. YOLOv3-A: a traffic sign detection network based on attention mechanism[J]. Journal on Communications, 2021, 42(1): 87-99.

Figures/Tables 18

特征压缩方法	Precision	Recall
GMaxPool	86.9%	89.8%
GAvgPool	86.7%	89.6%
GMaxPool + GAvgPool	87.0%	90.1%
Concat(GmaxPool, GAvgPool)	$87 . 2 %$	$90 . 3 %$

方法	小目标	中目标	大目标	整体尺度目标
文献[25]方法	0.854	0.921	0.915	0.897
RetinaNet101^[8]	0.891	0.957	0.873	0.920
YOLOv3^[14]	0.876	0.952	0.914	0.917
YOLOv3-A	$0 . 911$	$0 . 965$	$0 . 938$	$0 . 941$

References 28

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方法	CA	MGSA	Precision	Recall
YOLOv3	—	—	86.6%	89.4%
YOLOv3-CA	√	—	87.2%	90.3%
YOLOv3-MGSA	—	√	88.0%	91.6%
YOLOv3-A	√	√	88.5%	92.2%

YOLOv3-A: a traffic sign detection network based on attention mechanism

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