During the instance segmentation for contour convergence, it is a general problem that target occlusion increases the time for contour processing and reduces the accuracy of the detection box. This paper proposes an algorithm for real-time instance segmentation, adding fragment matching, target aggregation loss function and boundary coefficient modules to the processing contour. Firstly, fragment matching is performed on the initial contour formed by evenly spaced points, and local ground truth points are allocated in each fragment to achieve a more natural, faster, and smoother deformation path. Secondly, the target aggregation loss function and the boundary coefficient modules are used to predict the objects in the presence of object occlusion and give an accurate detection box. Finally, circular convolution and Snake model are used to converge the matched contours, and then the vertices are iteratively calculated to obtain segmentation results. The proposed method is evaluated on multiple data sets such as Cityscapes, Kins, COCO, et al, among which 30.7 mAP and 33.1 f/s results are obtained on the COCO dataset, achieving a compromise between accuracy and speed.