基于多尺度特征融合的小目标交通标志检测
Small-Target Traffic Sign Detection Based on Multiscale Feature Fusion
井方科 1任红格 2李松1
作者信息
- 1. 华北理工大学电气工程学院,河北 唐山 063210
- 2. 天津城建大学控制与机械工程学院,天津 300384
- 折叠
摘要
针对现有目标检测算法对小尺寸或特征不明显的交通标志检测效果较差的问题,提出一种基于多尺度特征融合的小目标交通标志检测算法.首先,设计一种双向自适应特征金字塔网络,充分利用细节特征和跳跃连接,增强多尺度特征融合;其次,针对小目标的尺度特点提出双头检测结构,聚焦小目标特征信息,同时减少模型的参数量;再次,使用Wise-IoU v3边界框损失函数,结合动态非单调聚焦机制,利用锚框梯度增益分配策略,减小低质量示例产生的有害梯度;最后,在特征提取网络中融入坐标卷积(CoordConv),提升网络对坐标信息的关注程度,从而增强模型的空间感知能力.在Tsinghua-Tencent 100K数据集上的实验结果表明,改进后的模型的平均精度均值(mAP)为87.7%,较YOLOv5s提升了2.2百分点,且参数量仅为6.3×107,达到了参数量更少、精度更高的检测效果.
Abstract
A small-target traffic sign detection algorithm based on multiscale feature fusion is proposed to address the limited effectiveness of the existing target detection algorithms in detecting traffic signs with small sizes or inconspicuous features.First,a bidirectional adaptive-feature pyramid network is designed to extract all detail features and jump connections to enhance multiscale feature fusion.Second,a dual-head detection structure is proposed for the scale characteristics of small targets,focusing on small-target feature information while reducing the number of model parameters.Next,using the Wise-IoU v3 bounding box loss function and a dynamic nonmonotonic focusing mechanism,the harmful gradients generated by low-quality examples are reduced by employing the anchor-box gradient gain allocation strategy.Finally,coordinate convolution(CoordConv)is incorporated into the feature extraction network to enhance the spatial awareness of the model by improving the network's focus on coordinate information.The experimental results on the Tsinghua-Tencent 100K dataset show that the improved model has a mean average precision(mAP)of 87.7%,which is a 2.2 percentage points improvement over YOLOv5s.Moreover,the number of parameters is only 6.3×107,thereby achieving a detection effect with fewer parameters and higher accuracy.
关键词
机器视觉/小目标/交通标志检测/多尺度特征融合/损失函数Key words
machine vision/small object/traffic sign detection/multi-scale feature fusion/loss function引用本文复制引用
基金项目
国家自然科学基金(61203343)
河北省自然科学基金(F2018209289)
出版年
2024
NETL
NSTL
万方数据