To address problems of low detection precision， coarse masks and weak generalization ability of the existing instance segmentation algorithms for occluded and blurred instances， an instance segmentation algorithm based on Fastformer and self-supervised contrastive learning was proposed. Firstly， in order to enhance the ability of algorithm to extract global information of feature maps， the Fastformer module based on additive attention was added after feature extraction network， and interrelationship between pixels in each layer of feature map was modeled deeply. Secondly， inspired by self-supervised learning， a self-supervised contrastive learning module was added to conduct self-supervised contrastive learning to instances in images to enhance the ability of algorithm to understand images， thereby improving segmentation results in environments with much noise interference. Experimental results show that the proposed algorithm has the mean Average Precision （mAP） improved by 3.1 and 2.5 percentage points respectively， compared to recently classical instance segmentation algorithm SOLOv2（Segmenting Objects by LOcations v2） on Cityscapes dataset and COCO2017 dataset. And a great balance is achieved between real-time performance and precision by the proposed algorithm， leading good robustness in segmentation instance of complex scenes.