| 改进CenterNet的交通标志检测算法 |
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| 引用本文： | 成怡,张宇,李宝全. 改进CenterNet的交通标志检测算法[J]. 信号处理, 2022, 38(3): 511-518. DOI: 10.16798/j.issn.1003-0530.2022.03.008 |
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| 作者姓名： | 成怡 张宇 李宝全 |
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| 作者单位： | 1.天津工业大学控制科学与工程学院, 天津 300387 |
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| 基金项目： | 国家自然科学基金61973234天津市自然科学基金18JCYBJC88400天津市自然科学基金18JCYBJC88300 |
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| 摘 要： | 针对交通标志尺度变化大导致检测精度低的问题,本文提出一种改进CenterNet的交通标志检测算法.采用ResNeSt50作为主干特征提取网络,引入PSConv(Ploy-Scale Convolution)改进网络卷积层结构.设计多尺度感受野模块,对ASPP(Atrous Spatial Pyramid Pooling...
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| 关 键 词： | 交通标志检测 多尺度感受野模块 特征增强模块 深度学习 |
| 收稿时间： | 2021-06-10 |
Traffic Sign Detection Algorithm Based on Improved CenterNet |
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| Affiliation: | 1.School of Control Science and Engineering，Tiangong University，Tianjin 300387，China2.Tianjin Key Laboratory of Intelligent Control of Electrical Equipment，Tianjin 300387，China |
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| Abstract: | Aiming at the problem of low detection accuracy caused by the large scale change of traffic signs， this paper proposed a traffic sign detection algorithm based on improved CenterNet. ResNeSt50 was used as the backbone of improved CenterNet， and PSConv（Ploy-Scale Convolution） convolution was introduced to improve convolutional layers structure of the network. To improve the detection ability of different scale signs， this paper designed the multi-scale receptive field module which selected the appropriate expansion rate for ASPP（Atrous Spatial Pyramid Pooling） and used the attention mechanism to optimize the output of the module. The feature enhancement module was designed in the decoding network to reduce the feature loss caused by continuous up sampling. In order to inaccurate target size in CenterNet regression， GHM（Gradient Harmonizing Mechanism） was used to improve the loss function. Experimental results show that the overall accuracy of the improved algorithm is increased by 9.45%， and the detection speed reaches 91.01 frames per second， which is suitable for traffic sign detection. |
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