Real-Time Instance Segmentation Method Based on Location Attention

Instance segmentation is a challenging research in the field of computer vision, which combines the prediction results of object detection and semantic segmentation to provide richer image feature information.Focusing on the instance segmentation in the street scene, the real-time instance segmentation method based on SOLOv2 is proposed in this paper.First, a cross-stage fusion backbone network based on position attention is designed to increase the model accuracy and reduce the computational effort.Then, the loss of shallow location information is decreased by integrating two-way feature pyramid networks.Meanwhile, crossstage mask feature fusion is designed to resolve the small objects missed segmentation.Finally, the adaptive minimum loss matching method is proposed to decrease the loss of segmentation accuracy due to object occlusion in the image.Compared with other mainstream methods, our method meets the real-time segmentation requirements and achieves competitive performance in segmentation accuracy.