使用深度视觉模型提升终端自动化测试能力的研究  

Research on Using Deep Vision Models to Improve Terminal Automation Test Capability

作　　者：邓华 DENG Hua(China Mobile Internet of Things Co.,LTD.,Chongqing 401100,China)

机构地区：[1]中移物联网有限公司,重庆401100

出　　处：《长江信息通信》2025年第1期96-98,105,共4页Changjiang Information & Communications

摘　　要：该文旨在探索深度视觉模型在终端自动化测试中替代人工视觉判断的可行性。通过对预训练的图像和视频识别模型进行微调,开发了新的视觉自动化测试工具。研究应用了目标检测、识别、跟踪及视频理解等模型,重点解决了花屏识别、机顶盒UI控制和指示灯状态识别等问题。实验结果表明,视觉模型在这些场景中表现出高准确率(>90%),能有效替代人工视觉测试。研究证实了深度视觉模型在提升终端自动化测试效率、准确度和溯源能力方面的潜力,为未来与大模型结合的智能测试系统奠定了基础。This study explores the feasibility of replacing manual visual judgment with deep vision models in terminal automated testing.New visual automated testing tools were developed by fine-tuning pre-trained image and video recognition models.The research applied object detection,recognition,tracking,and video understanding models,focusing on solving issues such as screen distortion identification,set-top box UI control,and indicator light status recognition.Experimental results show that visual models achieved high accuracy(>90%)in these scenarios,effectively substituting manual visual testing.The study confirms the potential of deep vision models in improving efficiency,accuracy,and traceability of terminal automated testing,laying the foundation for future intelligent testing systems integrated with large models.

关 键 词：深度视觉模型 终端自动化测试 人工视觉替代 

分 类 号：TN41[电子电信—微电子学与固体电子学]