基于改进Anchor-Free模型的交通标志检测算法

Traffic Sign Detection Algorithm Based on Modified Anchor-Free Model

交通标志检测是自动驾驶系统的一项重要功能,当前先进的交通标志检测器大多采用Anchor-Based网络模型,根据锚框遍历所有潜在的目标位置。为了减少锚框带来的计算开销和过多的超参数设置,提出了一种基于编码-解码结构的Anchor-Free交通标志检测算法。为了增加解码模块的特征表征能力,在解码模块中引入残差增强分支。为了高效地提取和利用多尺度特征,设计了特征融合子网络,提升对多尺度目标的检测能力,并使用Ghost轻量化模块提取多尺度特征图,不显著引进运算量。在Tsinghua-Tencent 100K数据集上进行验证,所提算法实现了92.5%的召回率和90.3%的准确率,模型的参数量和模型大小分别为1.61×10^(7)和64.4 Mbit。实验结果表明,与主流目标检测算法相比,所提算法的检测精度较高,计算开销较低,在综合性能上具有优越性。

Traffic sign detection is an essential function of autonomous driving systems,and most modern traffic sign detectors are anchor-based,traversing potential object locations based on anchors.To solve the problems of heavy computing costs and the need to set several hyperparameters in anchor-based models,we propose an anchor-free traffic sign detection algorithm based on an encoder-decoder structure.We introduce a residual augmentation branch in the decoder module in this study to improve feature expression ability during the decoding process.To improve the ability to detect multiscale traffic signs,we propose a multiscale feature fusion subnetwork to effectively extract and use multiscale features.A Ghost lightweight module is adopted by the multiscale feature extraction module,which indistinctively increases the computational cost.On the Tsinghua-Tencent 100 K dataset,our approach achieved a recall of 92.5% and an accuracy of 90.3%,while the model’s parameter amount and model size are approximately 1.61×10^(7)and 64.4 Mbit,respectively.The experimental results show that the proposed algorithm outperforms the mainstream object detection algorithms in terms of precision,computing cost,and overall performance.