Research And Implementation Of Traffic Video Super-Resolution Technology Based On Deep Learning

Due to weather changes,hardware limitations,and fast-moving vehicles on roads,traffic videos often suffer from false images,blurring,and other issues.Traffic video super-resolution technology can transform low-resolution blurry videos into high-resolution clear ones,not only providing users with better user experience,but also promoting the development of intelligent transportation and smart cities.Therefore,research on super-resolution technology for traffic videos has significant academic and engineering practical significance.Video super-resolution technology is a hot research and application topic in the field of image processing and has made great progress in recent years.However,there are still some problems and challenges,such as the lack of lightweight video super-resolution algorithms that can be deployed on resource-limited platforms,the lack of video super-resolution algorithms for arbitrary scale amplification,the lack of more reasonable degradation processes that cannot fully simulate the real-world degradation process,and the lack of more effective methods to utilize adjacent frame information to help recover video details.This thesis focuses on solving the fourth problem.The main work of this thesis is to study more effective algorithms that utilize adjacent frame information to help users obtain more and more effective information from traffic videos that suffer from motion blur and poor quality.The following work has been completed:First,the thesis proposes a traffic video super-resolution algorithm(TVSR)that is different from existing technology.On the one hand,the thesis designs a dual alignment module that combines optical flow estimation and deformable convolution to handle object motion and scene shift between adjacent frames.By using optical flow residuals to guide the learning of the offset,the module overcomes the unstable training issue of deformable convolution,achieving better alignment between adjacent frames and the current frame.On the other hand,the thesis designed a spatio-temporal feature aggregation module that combines the temporal attention mechanism with the spatial attention mechanism.This module focuses on the regions that are more effective for the restoration of the current frame from both temporal and spatial dimensions,allocates larger attention weights to these regions,and promotes the effective fusion of adjacent frames information.Second,based on the proposed TVSR algorithm,this thesis designs and implements a webbased prototype system.The requirements statement includes functional and performance requirements.System analysis and design include use case modeling,static class analysis,architectural design,system functional architecture,and database design.The system implementation is based on the B/S architecture for prototype development.System testing includes functional testing and performance testing,and the conclusion is that the prototype system meets standards for functionality,security,usability,and portability.Third,a traffic video super-resolution dataset is constructed.In view of the fact that there is currently no dataset specifically for traffic video super-resolution,a batch of datasets for the transportation field were constructed by various legal means,containing 27,000 video frames,which laid the foundation for subsequent research.The thesis work achieves the expected goal,with its innovations mainly encompassing theoretical innovation and application innovation.The theoretical innovation is manifested by a proposing super-resolution algorithm for traffic videos,which combines optical flow estimation and deformable convolution to deal with feature alignment,and combines temporal attention mechanism and spatial attention mechanism to deal with feature aggregation,and has good performance.The application innovation is demonstrated by extending the thesis’ s work from the video super-resolution project conducted during the internship in Nanjing Internship Base.It can be used for company trials and has good engineering application value and social benefits.