融合目标检测与距离阈值模型的露天矿行车障碍预警

针对当前行车预警方法无法适应露天矿非结构化道路问题,本文提出一种融合目标检测和障碍距离阈值的预警方法。首先根据露天矿障碍特点改进原有的Mask R-CNN检测框架,在骨架网络中引入扩张卷积,在不缩小特征图的情况下扩大感受野范围保证较大目标的检测精度。然后,根据目标检测结果构建线性距离因子,表征障碍物在输入图像中的深度信息,并建立SVM预警模型。最后为了保证预警模型的泛化能力采用迁移学习的方法,在COCO数据集中对网络进行预训练,在文中实地采集的数据集中训练C5阶段和检测层。实验结果表明,本文方法在实地数据检测中精确率达到98.47%,召回率为97.56%,人工设计的线性距离因子对SVM预警模型有良好的适应性。

An open-pit mine roadway obstacle warning method integrating the object detection and distance threshold model

In order to solve the problem that the current driving warning method cannot adapt to the unstructured road in open-pit mine,this paper proposes an early warning method that integrates target detection and obstacle distance threshold.Firstly,the original Mask R-CNN detection framework was improved according to the characteristics of open-pit mine obstacles,and dilated convolution was introduced into the framework network to expand the receptive field range without reducing the feature map to ensure the detection accuracy of larger targets.Then,a linear distance factor was constructed based on the target detection results to represent the depth information of obstacles in the input image,and an SVM warning model was established.Finally,in order to ensure the generalization ability of the warning model,transfer learning method was adopted to carry out pre-training of the network in COCO data set,and both the C5 stage and detection layer were trained in the data collected in the field.The experimental results show that the accuracy and recall of the proposed method reach 98.47%and 97.56%in the field data detection,respectively,and the manually designed linear distance factor has a good adaptability to the SVM warning model.