融合感受野增强和注意力机制的交通标志检测算法

doi:10.16180/j.cnki.issn1007-7820.2024.06.002

Abstract

Abstract:

In view of the number of shortcomings of target detection algorithm in traffic sign detection, this study proposes a traffic sign detection algorithm that incorporating receptive field enhancement module and attention mechanism. The algorithm is improved on basis of YOLOv5(You Only Look Once version 5) algorithm, the RFB (Receptive Field Block) is used to replace the SPP(Spatial Pyramid Pooling) in the original backbone, the attention mechanism modules ECAM(Efficient Channel Attention Module) and CBA (Convolutional Block Attention Module) are embedded in the feature fusion network, and the Matrix NMS (Matrix Non-Maximum Suppression) is used to sift the candidate bounding-boxes. The experimental results show that there is no change in the number of model parameters when compared with the original network, meanwhile, mean average precision of the algorithm reaches 82.31%, which is 8.59% higher than the original network, and the detection speed reaches 51.89 frame·s^-1. In addition, there is no false detection or missing detection in each test scenario, which proves that the generalization ability of the algorithm is also better than original algorithm, and the algorithm can perform real-time detection of traffic signs.

Key words: real-time traffic sign detection, enhanc receptive field, attention mechanism, feature fusion, matrix non- maximum suppression, YOLOv5, deep learning, real-time detection

CLC Number:

TP391

YE Yuxin, JU Zhiyong, LAI Ying. Traffic Sign Detection Algorithm Incorporating Receptive Field Enhancement Module and Attention Mechanism[J].Electronic Science and Technology, 2024, 37(6): 8-16.

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References 19

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类别	标志
指示类	i2、i4、i5、i10、il60、il80、il100、ip
警告类	p3、p5、p10、p11、p12、p19、p23、p26、p27、pr40、 pr50、pa14、pm20、pl30、pl60、pl80、pl40、pl50、 pl100、pl120、ph4、ph4.5、ph5、pb、pg、pn、pne
禁止类	w13、w55、w57、w59

改进策略	不同算法模型
RFB	-	ü	-	-	ü	ü
CBAM+ECA	-	-	ü	-	ü	ü
Matrix-NMS	-	-	-	ü	-	ü
mAP/%	73.72	77.82	76.89	72.45	80.57	82.31
检测速度/ frame·s^-1	44.94	44.41	42.38	57.34	41.05	51.89

交通标志类别	F1-score		提升率 /%
交通标志类别	YOLOv5	本文	提升率 /%
i2	0.71	0.81	14.08
i4	0.88	0.87	-1.14
i5	0.89	0.91	2.25
i10	0.67	1.00	49.25
il60	0.87	0.91	4.60
il80	0.80	0.84	5.00
il100	0.86	0.88	2.33
ip	0.70	0.75	7.14
p3	0.55	0.76	38.18
p5	0.91	0.90	-1.10
p10	0.50	0.77	54.00
p11	0.56	0.63	12.50
p12	0.36	0.54	50.00
p19	0.50	0.73	46.00
p23	0.80	0.83	3.75
p26	0.69	0.79	14.49
p27	0.18	0.67	272.22
pa14	0.86	1.00	16.28
pb	0.92	0.92	0.00
pg	0.90	0.83	-7.78
ph4.5	0.72	0.75	4.17
ph4	0.36	0.67	86.11
ph5	0.24	0.67	179.17
pl30	0.56	0.59	5.36
pl40	0.69	0.73	5.80
pl50	0.54	0.65	20.37
pl60	0.56	0.70	25.00
pl80	0.74	0.81	9.46
pl100	0.82	0.85	3.66
pl120	0.65	0.80	23.08
pm20	0.79	0.81	2.53
pn	0.81	0.82	1.23
pne	0.94	0.94	0.00
pr40	0.94	0.97	3.19
pr50	0.67	0.67	0.00
w13	0.38	0.59	55.26
w55	0.44	0.73	65.91
w57	0.71	0.77	8.45
w59	0.76	0.86	13.16

模型	mAP/%	检测时间/ms	检测速度/ frame·s^-1
YOLOv5	73.72	22.25	44.94
本文模型	82.31	19.27	51.89

算法模型	参数量 /10⁶	mAP /%	检测时间 /ms	检测速度 /frame·s^-1
Faster-RCNN	137.099	87.44	161.70	6.18
Centernet	32.665	68.09	30.13	33.18
SSD	26.285	71.63	22.88	43.70
Efficientdet	3.874	64.51	25.36	39.42
YOLOv5	47.057	73.72	22.25	44.94
本文模型	47.139	82.31	19.27	51.89

Traffic Sign Detection Algorithm Incorporating Receptive Field Enhancement Module and Attention Mechanism

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