摘要
针对现有煤矿井下无人驾驶轨道电机车因巷道环境恶劣导致障碍物识别精度低的问题,提出用于无人驾驶电机车障碍物精准实时检测的PDM-YOLO模型.基于YOLOv5,将模型C3模块中的传统卷积替换为部分卷积,构建C3_P特征提取模块,有效减少模型的浮点运算量(FLOPs)与计算延迟.采用改进后的解耦头,对传统YOLOv5的预测头进行解耦,提高模型的收敛速度及对障碍物的识别精度.优化Mosaic数据增强方法,丰富训练图像的特征信息,提高模型的普适性和鲁棒性.实验结果表明,PDM-YOLO模型在自制数据集上的平均检测精度(mAP)达到96.3%,平均检测速度达到109.2帧/s,PDM-YOLO模型在PASCAL VOC公共数据集上的检测精度高于现有主流的YOLO系列模型.
The PDM-YOLO model for accurate real-time obstacle detection in unmanned electric locomotives was proposed in order to address the problem of low accuracy of obstacle recognition in existing coal mine underground unmanned electric locomotives due to poor roadway environments.The ordinary convolution in the C3 module of the conventional YOLOv5 model was replaced with partial convolution to construct the C3_P feature extraction module,which effectively reduced the floating-point operations(FLOPs)and computational delay of the model.The improved decoupled head was used to decouple the prediction head of the conventional YOLOv5 model in order to improve the convergence speed of the model and the accuracy of obstacle recognition.The Mosaic data augmentation method was optimized to enrich the feature information of the training images and enhance the generalizability and robustness of the model.The experimental results showed that the mean average precision(mAP)of the PDM-YOLO model reached 96.3%and the average detection speed reached 109.2 frames per second on the self-built dataset.The detection accuracy of the PDM-YOLO model on the PASCAL VOC public dataset is higher than that of the existing mainstream YOLO series models.
作者
杨豚
郭永存
王爽
马鑫
YANG Tun;GUO Yongcun;WANG Shuang;MA Xin(State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines,Anhui University of Science and Technology,Huainan 232001,China;Anhui Key Laboratory of Mine Intelligent Equipment and Technology,Anhui University of Science and Technology,Huainan 232001,China;School of Mechanical Engineering,Anhui University of Science and Technology,Huainan 232001,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2024年第1期29-39,共11页
Journal of Zhejiang University：Engineering Science
基金
安徽省高校杰出青年科研资助项目(2022AH020056)
国家自然科学基金资助项目(52274152)
中国科协青年托举人才工程资助项目(YESS20220337)。
