| Abstrakt: |
The popularity of deep learning and cloud computing has promoted the widespread application of computer vision in various industries. However, centralized cloud inference services have problems such as high bandwidth resource consumption, image data privacy leakage, and high latency. It is hard that satisfy demand which requires diversified computer vision application. The dual gigabit upgrade of the communication network will promote depth collaboration of computer vision cloud-edge algorithms. Aiming to study the computer vision inference mechanism based on cloud-edge collaboration. Firstly, the advantages and disadvantages of the mainstream cloud and edge computer vision inference models in recent years were analyzed and explained, and on this basis, research on the cloud-edge collaborative computer vision inference model framework and deployment mechanism was carried out, model distributed reasoning model segmentation strategy, cloud-side collaborative network deployment optimization strategy was discussed in detail. In the end, the challenge and prospect of deep learning cloud-edge collaboration inference in future was discussed through data collaboration, network partition collaboration, and business function collaboration. [ABSTRACT FROM AUTHOR] |
