俞林森,陈志国.融合前景注意力的轻量级交通标志检测网络[J].电子测量与仪器学报,2023,37(1):21-31 |
融合前景注意力的轻量级交通标志检测网络 |
Lightweight traffic sign detection network with fused foreground attention |
|
DOI:10.13382/j.issn.1000-7105.2023.01.003 |
中文关键词: 交通标志检测 激活函数 前景注意力 特征融合 VariFocalLoss GIoU |
英文关键词:traffic sign detection activation function foreground attention feature fusion VariFocalLoss GIoU |
基金项目:国家自然科学基金(62073155)项目资助 |
|
|
摘要点击次数: 1015 |
全文下载次数: 770 |
中文摘要: |
针对目标检测算法模型在交通标志检测上容易出现错检和漏检等问题,提出一种融合前景注意力的轻量级交通标志检
测网络 YOLOT。 首先引入 SiLU 激活函数,提升模型检测的准确率;其次设计了一种基于鬼影模块的轻量级骨干网络,有效提
取目标物特征;接着引入前景注意力感知模块,抑制背景噪声;然后改进路径聚合网络,加入残差结构,充分学习底层特征信息;
最后使用 VariFocalLoss 和 GIoU,分别计算目标的分类损失和目标间的相似度,使目标的分类和定位更加准确。 在多个数据集
上进行了大量实验,结果表明,本文方法的精度优于目前最先进方法,在 CCTSDB 数据集上进行消融实验,最终精度达到
98. 50%,与基线模型相比,准确率提升 1. 32%,同时模型仅 4. 7 MB,实时检测帧率达到 44 FPS。 |
英文摘要: |
A lightweight traffic sign detection network incorporating foreground attention, YOLOT, is proposed to address the problem
that object detection algorithm models are prone to error and miss detection on traffic sign detection. Firstly, the introduction of the SiLU
activation function to improve the accuracy of model detection; secondly, a lightweight backbone network based on the ghost module is
designed to effectively extract object features; thirdly, introduction of foreground attention perception module to suppress background
noise; fourthly, we improve the path aggregation network by adding a residual structure to the feature fusion process; finally, we use
VariFocalLoss and GIoU to calculate the classification loss of objects and the similarity between objects. Extensive experiments are
conducted on several datasets, and the results show that the accuracy of the method in this paper is better than the current state-of-the-art
methods. Ablation experiments are conducted on the CCTSDB dataset, and the final accuracy reaches 98. 50%, with an accuracy
improvement of 1. 32% compared to the baseline model, while the model is only 4. 7 MB, and the real-time detection frame rate reaches
44 frames per second. |
查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|