In view of the overlapping multiple targets in unstructured road images and the large difference in scale , it is easy to miss or misdetect and poor segmentation accuracy. An improved SOLOv2 instance segmentation algorithm is proposed. Firstly , a bottom-up enhancement path is added to the feature pyramid structure to reduce the loss of fea- ture transfer process , and secondly , dual attention is used to guide feature selection , adaptive selection of important features , suppression of redundant information , improvement of the extraction ability of detailed features , and enhance- ment of feature representation of category branches and mask branches , so as to improve the accuracy of mask predic- tion. In addition , the unstructured road image dataset is preprocessed to improve the generalization ability of the mod- el. The experimental results show that the proposed method is more accurate in controlling the instance boundary , and the average accuracy of SOLOv2 and Mask-RCNN is increased by 2. 0% and 2. 2% , respectively , and the detection frame rate is increased to 6. 1 frames/s , which has good segmentation performance in different environments.