针对小目标交通标志检测存在的检测精度低、实时性差及模型体积大等问题,提出一种基于Yolov5的实时道路交通标志检测算法.首先,对Mobilenetv3中的倒残差结构进行改进,将其应用于Yolov5的主干网络中,使其更符合轻量网络的设计要求;其次,使用轻量级上采样通用算子CARAFE(content-aware ReAssembly of FEatures)代替原始网络的最近邻插值上采样模块,减少上采样信息损失的同时增大感受野;最后,使用全局与局部融合注意力(GLFA)聚焦全局尺度与局部尺度,增强网络对小目标物体的敏感程度.在自制中国多类交通标志数据集(CMTSD)上的实验结果表明:相比改进前的算法,改进后的算法在模型体积减小8.76 MB的基础上,平均精度均值(mAP)@0.5提升了2.58百分点,检测速度达62.59 frame/s;与其他主流目标检测算法相比,该算法在检测精度、检测速度及模型体积上具有一定的优势,在真实复杂交通场景中具有较好的性能.
Real-Time Traffic Sign Detection Based on Yolov5-MGC
For handling low detection accuracy,poor real-time performance,and large model size of small target traffic sign detection,a real-time road traffic sign detection algorithm based on Yolov5 is proposed.First,the inverted residual structure in Mobilenetv3 was improved and applied to the backbone network of Yolov5 to align with the lightweight network design.Then,the lightweight upsampling universal operator CARAFE(content-aware ReAssembly of FEatures)replaced the nearest neighbor interpolation upsampling module of the original network,reducing the loss of upsampling information and increasing the receptive field.Finally,global and local fusion attention(GLFA)was used to focus on the global and local scales to enhance the sensitivity of the network for small target objects.Experiments on the self-made Chinese multiclass traffic sign dataset(CMTSD)show that the enhanced algorithm improves mean accuracy precision(mAP)@0.5 by 2.58 percentage points based on the model size reduction of 8.76 MB compared with the algorithm before enhancement.Furthermore,the detection speed reaches 62.59 frame/s.Compared with other mainstream object detection algorithms,the proposed algorithm exhibits certain advantages in detection accuracy,speed,and model volume and performs better in real complex traffic scenes.
traffic sign detectionlightweight networkCARAFE operatorglobal and local fusionreal-time detection
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