改进YOLOv2算法的道路摩托车头盔检测

Improved YOLOv2 algorithm for road motorcycle helmet detection

针对摩托车头盔的传统检测方法准确率低、泛化能力差和目标检测网络参数量大难以在嵌入式设备运行的问题,提出改进的YOLOv2的MNXt-ECA-D-YOLOv2目标检测算法模型。首先引入Mobile Ne Xt网络替换YOLOv2原始骨干网络,其次在Mobile Ne Xt的沙漏块中引入密集连接结构同时在网络中引入有效通道注意力机制,然后在不同深度网络层应用不同的激活函数,最后在网络输出卷积层之前增加Drop Block模块。采用K-means聚类算法重新设计了自制数据集的先验框尺寸。实验结果表明,改进后的模型相比原始YOLOv2,在AP50指标上提高了3.53%,模型大小减少77.44%,检测速度提高了近4倍。通过对比实验可知,改进后的YOLOv2模型在保持较高的精度下模型更小,在CPU中的推理速度更快,具有一定的应用价值。

Aiming at the problems that the traditional detection methods of motorcycle helmet detection have low accuracy,poor generalization ability and large number of target detection network parameters,which are difficult to run on embedded devices,an improved MNXt-ECA-D-YOLOv2 target detection algorithm model of YOLOv2 is proposed.First,Mobile Ne Xt network is introduced to replace original YOLOv2 backbone network,and a densely connected network structure is introduced into the sandglass block of Mobile Ne Xt.At the same time,the effective channel attention mechanism is introduced into the network.And,different activation functions are applied at different depth network layers.Finally,Drop Block module is added before the network output convolutional layer.K-means clustering algorithm is adopted to redesign the anchor box size of self-made dataset.The experimental results show that compared with the original YOLOv2 under the same experimental conditions,the proposed method improves the AP50 metric by 3.53%and the model size reduced by 77.44%,and the detection speed increased by nearly 4 times.Comparison experiments demonstrate that the improved YOLOv2 has a higher average accuracy rate,a smaller model,and faster inference speed in CPU.Therefore,the proposed improved YOLOv2 model is valuable in practical applications.