Abstract: The emerging of autonomous driving for mining trucks in recent years has made obstacle detection critical. Unstructured roads in open pit areas are complex and variable, and small targets or multi-scale driving obstacles such as stones and potholes often appear, which seriously endanger driving safety. Therefore, this paper proposes a method for detecting road obstacles in open-pit mines based on bidirectional feature fusion. The open pit barrier image dataset is further segmented and expanded through the field acquisition and usage of data augmentation methods, and a RepVGG+ backbone network structure more suitable for obstacle detection is proposed in the feature extraction stage. In the feature fusion stage, a bidirectional feature fusion pyramid model based on SimAM space with channel attention and cross-stage connectivity is proposed. The performance of small target obstacle detection is improved by expanding the feature map and feature perception field for predicting small target obstacles, and the performance of multi-scale detection is improved by a bidirectional feature fusion mechanism. The convolutional layer and anchor size of the network classification and prediction module are also further adjusted to improve obstacle detection performance, reduce feature redundancy, and speed up model inference. In terms of the loss function of the model, for the problems of unbalanced samples in training and imprecise obstacle bounding box localization, the Focal Loss with fused label smoothing regularization is used as the classification loss function, and the GIoU Loss is used as the bounding box loss function to further optimize the open pit obstacle model. Experiments show that the method in this paper can effectively identify unstructured road obstacles in open pit mines in complex backgrounds. In practical applications, the detection accuracy reaches 91.76% mAP and the detection speed reaches 56.76 fps, which has a better small target and multi-scale target detection performance compared with some mainstream detection methods, and can meet the requirements for the safe detection of obstacles in the unmanned mine trucks traveling in open pit mines.