Page 222 - AI for Good-Innovate for Impact
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AI for Good-Innovate for Impact



                      (continued)

                       Domain                       Industry, Innovation, and Infrastructure
                       Model Training and fine-tuning  •  AIGC technology is proposed for the production of
                                                       training data of small sample to improve AI recognition
                                                       ability by using large models to solve problems such as
                                                       insufficient sample size and data skew in the traditional
                                                       visual AI training process.
                                                    •  Based on the open-source target detection model,
                                                       secondary training is carried out to label small targets
                                                       in images, and the location and category of targets
                                                       are usually marked with bounding box. The annotation
                                                       results are saved in VOC or COCO formats.

                       Case Studies                 Computer network fusion video brain

                       Testbeds or pilot deployments  The pilot is deployed on China Mobile's internal network.


                      52�2� Use case description


                      52�2�1  Description


                      Introduction: Guangdong company innovates to create computer and network integration
                      video intelligence brain, and carries out "artificial intelligence + video" action to promote
                      industrial upgrading and improve the quality of life. The platform sinks the video decoding
                      frame extraction and AI inference service computing power to the city node, realizing the
                      optimal and intelligent scheduling of video analysis computing resources at the provincial
                      side, the city edge side, and the user side, effectively saving 60% of bandwidth resources and
                      increasing the delay by 30%. Moreover, by deploying large models in the cloud, secondary
                      verification of recognition results is carried out to improve the accuracy of video intelligent
                      recognition. At the same time, in terms of data, the introduction of AIGC technology drives
                      the production of training data of small sample AI recognition ability by using large models
                      to solve problems such as insufficient sample size and data skew in the traditional visual AI
                      training process.

                      The project has landed in urban management and public safety and other fields, building smart
                      transportation, smart city and other business scenarios, to provide more comfortable living
                      conditions for urban residents; As well as industrial manufacturing and food production and
                      other fields, optimize the production process, efficiently supervise the production environment
                      and production quality and other factors, accelerate the industrialization process and improve
                      food safety. Subsequently, it can be extended to all walks of life to provide new quality
                      productivity for the development of the industry.

                      UN Goals:
                      •    Goal 3: Good health and well-being
                      •    Goal 9: Industry, Innovation, and Infrastructure
                      •    Goal 11: Sustainable cities and communities

                      Justify UN Goals selection: This project has created a computing network integration of video
                      intelligence, a one-stop video AI product enabling system as the design concept, through
                      "one cloud, two libraries, three centers" as the infrastructure, with "platform and equipment",




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