A Single-stage Instance Segmentation Method Based On Deep Learning

In recent years,deep learning has been widely used in the field of computer vision.Especially with the rapid development of computer hardware computing power,more and more industries have carried out intelligent industrial upgrading,which greatly optimizing the production process and application scenarios of products.At present,deep neural network has been widely used in intelligent medical,automatic driving,intelligent transportation,intelligent security inspection and other scenes.As a comprehensive image processing task,instance segmentation plays a huge role in these industries.Instance segmentation unifies the three visual tasks of classification,detection and segmentation,it can recognize the instances in the image at the pixel level,which is more in line with human visual cognition.At present,the existing instance segmentation methods have complex structure and large amount of computation,it can not achieve real-time processing.However,the inference speed of the model is very important for most application scenarios.Therefore,in view of the existing problems,this thesis designs a real-time high-performance single-stage instance segmentation method by optimizing the training process and network structure of the model.Following the idea of single-stage method,this thesis designs a more efficient instance segmentation method based on the SOLO v2 framework.This thesis mainly completes the following works:(1)In order to provide more high-quality positive samples for model training stage,this thesis designs an adaptive sample assignment strategy ASA for instance segmentation task.Experiments show that the ASA strategy can effectively improve the segmentation accuracy without increasing the inference time.(2)This thesis proposes a high-performance single-stage instance segmentation method E-Seg based on SOLO v2 framework.This thesis designs an enhanced feature pyramid module to enrich the diversity of features,and a progressive mask generation network to improve the quality of the prototype mask.E-seg improves the accuracy of instance segmentation while ensuring the inference speed.Finally,the accuracy of ESeg on COCO 2017 validation set is 36.6 m AP,and the inference time on 1080 TI is 32 ms.(3)This thesis proposes an instance segmentation toolbox FlySeg based on Pytorch.FlySeg modularizes the process of building the model.We can design the model by building blocks,which greatly improves the efficiency of algorithm research.The accuracy difference between the reproduced models in FlySeg and the officially published models is less than 0.5 m AP,which proves the effectiveness of FlySeg.