An improved road network extraction method of open-pit mine based on DeepLabv3+ is proposed to address road boundary information loss and inaccurate road network extraction during road extraction of open-pit mine images captured using unmanned aerial vehicle. First, the Retinex algorithm is used to preprocess the original images to get a dataset with balanced color and light. Second, because the proportion of pixels between the road region and background are largely different, the proportion weighting method is used to correct the serious imbalance between positive and negative samples in network training. Finally, a densely connected ASPP module with different rates is built using the DeepLabv3+ model to optimize the feature extraction of the open-pit mine road, thereby expanding the receptive field of the road feature points and improving the coverage of multiscale features. Experimental results show that the semantic segmentation method is better than the original DeepLabv3+ algorithm. The mean intersection over union reaches 79.27%, accurately extracting road areas in a large range. The proposed method can be applied to extract the main road network of open-pit mines.