激光与光电子学进展, 2020, 57 (10): 101010, 网络出版: 2020-05-08
基于改进YOLOv2模型的多目标识别方法 下载: 980次
Multi-Target Recognition Method Based on Improved YOLOv2 Model
图像处理 智能交通 多目标识别 YOLOv2 深度学习 iamge processing intelligent traffic multi-target recognition YOLOv2 deep learning
摘要
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。
Abstract
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.
李珣, 时斌斌, 刘洋, 张蕾, 王晓华. 基于改进YOLOv2模型的多目标识别方法[J]. 激光与光电子学进展, 2020, 57(10): 101010. Xun Li, Binbin Shi, Yang Liu, Lei Zhang, Xiaohua Wang. Multi-Target Recognition Method Based on Improved YOLOv2 Model[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101010.