基于改进YOLOv5的高速公路隧道车辆和人员检测

Vehicle and Personnel Detection in Highway Tunnels Based on Improved YOLOv5

针对高速公路隧道内光线昏暗、图像受灯光影响及远距离小目标检测困难等问题,提出了一种改进的YOLOv5高速公路隧道车辆和人员检测算法。首先,使用高斯混合聚类来获得更加匹配数据集目标的一组锚框,提高了模型的检测精度;其次,在特征融合部分引入内容感知重组特征(content-aware ReAssembly of FEatures, CARAFE)上采样算子,扩大感受野,降低上采样过程特征细节损失;最后,通过向网络中插入坐标注意力(coordinate attention, CA),进一步增强模型对图像各位置特征的提取能力。为验证算法的有效性,在浙江温丽高速公路隧道数据集上进行实验,结果表明:所提算法的平均检测精度(mean average precision, mAP)达到了95.7%,较原模型提升3.8%,对于远距离小目标和受严重灯光影响的目标能够实现更加精准检测,为复杂环境下高速公路隧道内车辆和人员检测提供了一种有效的解决方案。

To address the problems of dim light,image affected by lamp light and difficulty in detecting small targets at long distance in highway tunnel,an improved YOLOv5 algorithm for vehicle and personnel detection in highway tunnel was proposed.Firstly,Gaussian mixture clustering was used to obtain a set of anchor boxes that match the dataset targets better,improving the detection accuracy of the model.Secondly,CARAFE operator was introduced in the feature fusion to enlarge the receptive field and reduce the loss of feature detail information in the upsampling process.Finally,by inserting coordinate attention(CA)into the network,the model s ability to extract features from different positions of the image is further enhanced.To verify the effectiveness of the algorithm,experiments were conducted on the Zhejiang Wenli Expressway Tunnel dataset,and the results show that:the mean average precision(mAP)of the proposed algorithm reaches 95.7%,which is 3.8%higher than the original model,and it can achieve more accurate detection for small targets at long distances and targets affected by severe lighting,providing an effective solution for vehicle and personnel detection in highway tunnels under complex environments.