Vehicle multi-target detection method based on YOLO v2 algorithm under darknet framework
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摘要: 针对道路车辆目标检测传统方法需随场景变化提取不同特征, 检测率较低与鲁棒性差的问题, 提出了一种基于Darknet框架下YOLO v2算法的车辆多目标检测方法; 根据目标路段场景与车流量的变化对YOLO-voc网络模型进行改进, 基于ImageNet数据集和微调技术获得分类训练网络模型, 对训练结果和车辆目标特征进行分析后进一步调整改进的算法参数, 最终获得更适合于道路车辆检测的YOLO-vocRV网络模型下车辆多目标检测方法; 为验证检测方法的有效性和完备性, 采用不同车流密度进行了车辆多目标检测试验, 并与经典YOLO-voc、YOLO9000模型进行了对比; 采用改进YOLO-vocRV网络模型, 选取20 000次迭代, 分析了多目标检测结果。试验结果表明: 在阻塞流样本条件下, YOLO9000网络模型检测率为93.71%, YOLO-voc网络模型检测率为94.48%, 改进YOLO-vocRV网络模型检测率达到了96.95%, 因此, 改进网络模型YOLOvocRV检测率较高; YOLO-vocRV模型精确度和召回率均聚集在0.95, 因此, 在获得较好精确度的条件下损失的召回率明显较小, 达到了很好的折中; 采用混合样本训练后, 基于YOLO-vocRV模型的车辆多目标检测方法的检测率在自由流状态下可达99.11%, 同步流状态下可达97.62%, 阻塞流状态下可达到97.14%, 具有较小的误检率和良好的鲁棒性。Abstract: To improve the detection rate and robustness of the traditional road vehicle detection methods that need to extract different features as the scenes change, a vehicle multi-target detection method based on the YOLO v2 algorithm under the darknet framework was proposed.The YOLO-voc network model was improved according to the changes of the scenes and traffic flows of target road sections.The classification training model was obtained based on the ImageNet data and fine-tuning technology.The parameters of the improved algorithm were adjusted according to the training results and vehicle target characteristics.Lastly, the vehiclemulti-target detection method in YOLO-vocRV network model was obtained and more suitable for road vehicle detection.In order to verify the validity and completeness of the detection method, the vehicle multi-target detection experiment was carried based on different traffic densities.At the same time, the improved method was compared with the YOLO-voc and YOLO9000 model.The multi-target detection test result was analyzed by the improved YOLO-vocRV network model based on 20 000 iterations.Test result shows that in the blocking flow condition, the detection rates of the YOLO9000 network model, YOLO-voc network model and improved network model YOLO-vocRV are 93.71%, 94.48% and 96.95%, respectively, so the detection rate of the YOLO-vocRV model is the highest.The precision and recall rate of the YOLO-vocRV model are gathered at 0.95, so it loses less recall rate under the condition of obtaining better precision, and agood compromise is achieved.After the mixed-sample training, the detection rate of vehicle multi-target detection method based on the YOLO-vocRV model is 99.11% in the free flow state, 97.62% in the synchronous flow state, and 97.14% in the blocking flow state, so it has little false detection rate and good robustness.
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图 10 自由流条件下不同模型验证结果
Figure 10. Verification results of different models under free flow condition
图 11 阻塞流条件下不同模型验证结果
Figure 11. Verification results of different models under blocking flow condition
图 12 自由流训练样本下YOLO9000模型测试结果
Figure 12. Test results of YOLO9000model under free flow training samples
图 13 自由流训练样本下YOLO-voc模型测试结果
Figure 13. Test results of YOLO-voc model under free flow training samples
图 14 自由流训练样本下YOLO-vocRV模型测试结果
Figure 14. Test results of YOLO-vocRV model under free flow training samples
图 15 同步流训练样本下YOLO9000模型测试结果
Figure 15. Test results of YOLO9000model under synchronous flow training samples
图 16 同步流训练样本下YOLO-voc模型测试结果
Figure 16. Test results of YOLO-voc model under synchronous flow training samples
图 17 同步流训练样本下YOLO-vocRV模型测试结果
Figure 17. Test results of YOLO-vocRV model under synchronous flow training samples
图 18 阻塞流下训练20 000次YOLO9000模型的测试结果
Figure 18. Test results of YOLO9000model under block flow and training iteration 20 000times
图 19 阻塞流下训练20 000次YOLO-voc模型的测试结果
Figure 19. Test results of YOLO-voc model under block flow and training iteration 20 000times
图 20 阻塞流下训练20 000次YOLO-vocRV模型的测试结果
Figure 20. Test results of YOLO-vocRV model under block flow and training iteration 20 000times
表 2 同步流下不同模型训练样本结果
Table 2. Results of different models under synchronous flow training samples
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表 4 混合训练样本下YOLO-vocRV模型测试结果
Table 4. Test results of YOLO-vocRV model under mixed training samples
下载: 导出CSV
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