计算机科学 ›› 2023, Vol. 50 ›› Issue (3): 238-245.doi: 10.11896/jsjkx.230100064
陈亮1, 王璐1, 李生春2, 刘昌宏2
CHEN Liang1, WANG Lu1, LI Shengchun2, LIU Changhong2
摘要： 仪表板是支持制造企业开展数据可视化分析和经营决策的重要手段。为解决可视化仪表板设计与实现过程中用户对专业知识依赖性强及流程迭代繁杂的问题,提出了一种基于深度学习技术YOLOv5s算法的可视化仪表板自动识别与生成方法。首先,基于YOLOv5s算法对仪表板图像及手绘草图中包含的可视化图表组件进行检测,并针对在检测过程中出现的手绘草图中不规则线条对识别图表的干扰及误检等问题,引入CA注意力机制来增强模型对重要特征的关注及目标精确定位能力,从而提高模型的识别精度;其次,将图表检测模型部署在Web中,服务器根据模型检测结果调用封装好的可视化图表组件代码,生成多组件组合的初始仪表板;最后,基于此Web设计,开发了一款数据可视化仪表板构建平台,为用户提供可修改配置仪表板样式及数据的详细选项,以方便用户快速构建完整的仪表板。通过收集Tableau,Power BI等可视化工具产生的仪表板图像及企业应用过程中手绘仪表板草图形成数据集,基于该数据集进行实验验证,改进的模型识别精度比原YOLOv5s模型提升了2.1%,mAP为98.4%,并通过系统部署应用验证了图表识别方法及开发的平台可有效识别及生成相应图表组件,满足用户的基本需求。
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