基于改进YOLOv2模型的多目标识别方法

在YOLOv2算法的基础上,根据实际道路环境的变化对YOLOv2-voc的网络结构进行改进,基于ImageNet数据集和微调技术得到分类训练网络模型,根据训练结果与车辆目标特征的分析,对算法参数进行修改,获得改进的车型识别分类网络结构模型YOLOv2-voc_mul。为验证所提模型的有效性,分别对简单背景和复杂背景下的样本进行检测,并与YOLOv2、YOLOv2-voc和YOLOv3模型在迭代70000次后的检测结果进行了对比。实验结果表明:在简单背景下,YOLOv2-voc_mul模型的精度可达99.20%,不同车型的平均精度均值达到了89.03%;在复杂背景下,YOLOv2-voc_mul模型对4种车型在单目标和多目标的检测下平均准确率达到了92.21%和89.44%,具有较高的精确度、较小的误检率和良好的鲁棒性。

Multi-Target Recognition Method Based on Improved YOLOv2 Model

Based on the YOLOv2 algorithm,the YOLOv2-voc network structure is improved according to the actual road-scene change.The classification training model is obtained based on ImageNet data and fine-tuning technology and in accordance with the analysis of the training results and target vehicle characteristics.Consequently,the improved vehicle identification classification network structure YOLOv2-voc_mul is obtained.Using samples from simple and complex backgrounds,experiments are conducted to verify the validity of the detection method.Further,the proposed model is compared with the YOLOv2,YOLOv2-voc,and YOLOv3 models after 70000 iterations.Results show that under simple background,the improved YOLOv2-voc_mul model has an accuracy of 99.20%and the mean average precision of different models achieves 89.03%.Under complex background,the improved YOLOv2-voc_mul model has average accuracies of 92.21%and 89.44%for the single-and multi-target detection of four different models,respectively.The proposed model shows excellent accuracy,small false detection rate,and good robustness.