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 istance 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.