基于深度学习的流程图线段检测方法OA

Flowchart line segment detection has been underexplored due to the scarcity of high-quality datasets and the weak interference resistance of endpoint labels.To address these challenges,this article proposes a flowchart line segment detection method based on label reshaping and dual-metric combined with the YOLOv5 model.Firstly,the FlowchartLine dataset containing 19 647 line segments is constructed to provide abundant training samples.Secondly,an endpoint-guided label reshaping module is introduced to transform the task from traditional endpoint detection to object detection,effectively mitigating the false detection issues caused by adjacent endpoints in traditional endpoint detection methods that lack holistic line segment representation.Finally,a geometry-aware enhanced joint loss is developed by coupling normalized Wasserstein distance(NWD)with original complete intersection over union(CIoU)loss to improve precise correction capability for line segment detection.Experimental results demonstrate that the proposed method achieves structural average precision scores of 98.2%,98.3%and 98.4%at sAP5,sAP10 and sAP15 thresholds respectively,outperforming current line segment detection method by 1.6%,1.0%and 0.9%,demonstrating superior accuracy in flowchart line segment detection.