基于通道注意力和特征增强的交通标志检测

doi:10.12141/j.issn.1000-565X.220639

摘要/Abstract

摘要：

道路上的交通标志包含大量的交通规则语义信息，快速、准确地获取这些信息有助于实现更高级别的辅助驾驶功能，从而提高车辆的安全性能。针对交通标志易受外界因素影响、类别间相似度高和尺寸微小的难点，本研究基于YOLOv5s模型，在数据预处理、特征提取、特征增强方面分别进行了针对性的改进。在数据预处理部分，利用颜色空间变换、几何变换矩阵来模拟实际场景中交通标志可能发生的颜色变化和形状变化，通过Mosaic算法、Copy-paste算法来提高训练集中微小交通标志的数量和背景的丰富性。在特征提取部分，构建了基于通道注意力标定的C3-TCA模块来提高模型对相似特征的辨别能力。在特征增强部分，通过双路径增强结构融合浅层特征和深层特征，并优化了预测分支的数量和下采样倍率，从而增加了对微小交通标志的检测精度。此外，还利用K-means++算法聚类先验框模板，基于CIoU度量构建边界框回归损失函数，从而降低边界框的回归难度。在TT100K和CCTSDB数据集上进行测试，模型的mAP@0.5指标分别为88.8%和83.5%，模型的检测速度分别为120.5　f/s和114.7　f/s。相较于现有交通标志检测模型，所构建模型在检测精度和检测速度上均达到了先进水平。针对数据增强算法、预测分支、通道注意力模块位置的对比实验进一步证明了所提具体优化方法的有效性。

关键词: 交通标志检测, 卷积神经网络, 通道注意力, 特征增强

Abstract:

Traffic signs on the road contain a large amount of semantic information about traffic rules, and rapid and accurate access to this information helps to achieve higher levels of assisted driving functions, thus improving vehicle’s safety performance. In view of the traffic signs are susceptible to external factors and the problems of high similarity between categories and small size, this paper made targeted improvements in data augmentation, feature extraction and feature enhancement based on YOLOv5s model. In the data augmentation part, color space transformation and geometric transformation matrix were used to simulate the possible color changes and shape changes of traffic signs in actual scenes, and the Mosaic algorithm and Copy-paste algorithm were used to improve the number of tiny traffic signs in the training set and the richness of the background. In the feature extraction part, a feature extraction module based on channel attention calibration was constructed to improve the model’s ability to discriminate similar features. In the feature enhancement part, the number of prediction branches and downsampling multiplier were optimized by fusing shallow features and deep features with a dual-path enhancement structure, so as to increase the detection accuracy of tiny traffic signs. In addition, the K-means++ algorithm was used to cluster the prior bounding box templates and construct the loss function based on the CIoU metric, thus reducing the difficulty of the prior bounding box regression. Experiments on the TT100K and CCTSDB dataset test show that the mAP@0.5 of the proposed model is 88.8% and 83.5% respectively, and the speed of the model is 120.5 f/s and 114.7 f/s respectively. Compared with the existing traffic sign detection models, the proposed model reaches the advanced level in both accuracy and speed. Comparison experiments for data augmentation algorithms, prediction branches, and channel attention module positions further demonstrate the effectiveness of the proposed specific optimization methods.

Key words: traffic sign detection, convolutional neural network, channel attention, feature enhancement

中图分类号:

TP391.41

罗玉涛, 高强. 基于通道注意力和特征增强的交通标志检测[J]. 华南理工大学学报(自然科学版), 2023, 51(12): 64-72.

LUO Yutao, GAO Qiang. Traffic Sign Detection Based on Channel Attention and Feature Enhancement[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(12): 64-72.

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代号	输出特征图尺寸	先验框尺寸
H2	160×160	（17，18）、（23，25）、（35，37）
H3	80×80	（10，12）、（12，20）、（15，16）
H4	40×40	（4，5）、（6，7）、（8，9）

模型	精确率/%	召回率/%	mAP@0.5/%	速度/（f·s^-1）
王卜等^［10］	56.1	86.7	82.0	36.8
陈梦涛等^［11］			86.6	34.5
YOLOv3^［5］	44.5	82.4	70.9	48.5
YOLOv4^［7］	81.8	85.4	82.1	36.2
YOLOv5s	84.0	71.3	79.4	128.2
TSDNet	90.1	83.9	88.8	120.5

模型	精确率/%	召回率/%	mAP@0.5/%	速度/（f·s^-1）
SSD^［9］	86.4	55.0	56.6	4.9
YOLOv3^［5］	84.6	42.7	50.5	20.3
YOLOv4^［7］	76.2	52.5	51.7	16.6
YOLOv5s	90.8	69.2	76.3	123.5
TSDNet	90.7	76.8	83.5	114.7

数据增强优化	预测分支优化	C3-TCA	K-means++	CIoU	mAP@0.5/%	mAP/%	参数量	FLOPs/10⁹
					79.4	60.1	7 141 794	16.7
√					82.0	62.1	7 141 794	16.7
√	√				85.8	65.0	5 442 346	18.4
√	√	√			88.1	67.6	5 117 541	18.1
√	√	√	√		88.2	68.1	5 117 541	18.1
√	√	√	√	√	88.8	68.5	5 117 541	18.1

+H2	-H5	mAP@0.5/%	mAP/%	小目标mAP/%	中目标mAP/%	大目标mAP/%	参数量	FLOPs/10⁹
		82.0	62.1	43.2	70.5	81.3	7 141 794	16.7
√		82.7	62.5	48.0	69.0	80.7	7 293 590	19.9
√	√	85.8	65.0	53.2	70.8	80.8	5 442 346	18.4