基于改进注意力机制的交通标志检测算法

Traffic sign detection algorithm based on improved attention mechanism

针对交通标志在某些场景中存在分辨率过低、被覆盖等环境因素影响导致在目标检测任务中出现漏检、误检的情况,提出一种基于改进注意力机制的交通标志检测算法。首先,针对交通标志因破损、光照等环境影响造成图像分辨率低从而导致网络提取图像特征信息有限的问题,在主干网络中添加注意力模块以增强目标区域的关键特征;其次,特征图中相邻通道间的局部特征由于感受野重叠而存在一定的相关性,用大小为k的一维卷积代替通道注意力模块中的全连接层,以达到聚合不同通道信息和减少额外参数量的作用;最后,在路径聚合网络(PANet)的中、小尺度特征层引入感受野模块来增大特征图的感受野以融合目标区域的上下文信息,从而提升网络对交通标志的检测能力。在中国交通标志检测数据集(CCTSDB)上的实验结果表明,所提出的YOLOv4(YouOnlyLookOncev4)改进算法在引进极少的参数量与原算法检测速度相差不大的情况下,平均精确率均值(mAP)达96.88%,mAP提升了1.48%;而与轻量级网络YOLOv5s相比,在单张检测速度慢10 ms的情况下,所提算法mAP比YOLOv5s高3.40个百分点,检测速度达到40frame/s,说明该算法完全满足目标检测实时性的要求。

In some scenes,the low resolution,coverage and other environmental factors of traffic signs lead to missed and false detections in object detection tasks.Therefore,a traffic sign detection algorithm based on improved attention mechanism was proposed.First of all,in response to the problem of low image resolution due to damage,lighting and other environmental impacts of traffic signs,which leaded to the limited extraction of image feature information by the network,an attention module was added to the backbone network to enhance the key features of the object area.Secondly,the local features between adjacent channels in the feature map had a certain correlation due to the overlap of the receptive fields,a one-dimensional convolution of size k was used to replace the fully connected layer in the channel attention module to aggregate different channel information and reduce the number of additional parameters.Finally,the receptive field module was introduced in the medium-and small-scale feature layers of Path Aggregation Network(PANet)to increase the receptive field of the feature map to fuse the context information of the object area and improve the network’s ability to detect traffic signs.Experimental results on CSUST Chinese Traffic Sign Detection Benchmark(CCTSDB)dataset show that the proposed improved You Only Look Once v4(YOLOv4)algorithm achieve an average detection speed with a small amount of parameters introduced and the detection speed is not much different from that of the original algorithm.The mean Accuracy Precision(mAP)reached 96.88%,which was increased by 1.48%;compared with the lightweight network YOLOv5s,with the single frame detection speed of 10 ms slower,the mAP of the proposed algorithm is 3.40 percentage points higher than that of YOLOv5s,and the speed reached 40 frame/s,indicating that the algorithm meets the real-time requirements of object detection completely.