基于深度学习的电气二次图纸语义识别方法

Small target area extraction and semantic recognition method of electrical secondary drawings based on deep learning

图像文字识别及深度学习技术逐步应用在工程图纸识别领域。针对电气二次图纸语义识别中存在的小目标检测、文字背景复杂等问题,首先,提出面向电气有效信息的图纸小目标区域双层提取模型,上层模型为基于自适应阈值及轮廓检测的端子排单连通小目标区域提取,下层模型为基于双层目标检测网络的端子排表格及连接线文字小目标子区域提取。接着,提出基于单元格提取及Sobel算子边缘检测的端子排表格区域文字位置检测算法与基于水平垂直投影分割算法及方向旋转的端子排连接线文字区域的文字位置检测算法。最后利用所提算法对30张有标注图纸进行语义提取测试,测试集平均漏检率与正确臃的加权平均值为91.25%,测试集平均交并比平均值为82.61%,验证了所提算法的有效性及鲁棒性。

Image text recognition and deep learning technology are gradually applied in the field of engineering drawing recognition.The electrical secondary drawing takes the terminal block drawing as an example,which has problems such as small target detection and complex text background.Aiming at the problem of small target detection,a double-layer extraction model of small target area in drawings oriented to electrical effective information is proposed.The upper level model is the extraction of single-connected small target areas based on the adaptive threshold and contour detection,and the lower level model is the extraction of the terminal strip table and the small target sub-area of the connection line text based on the double-layer target detection network.Aiming at the complex text background,the text position detection of terminal row table area based on cell extraction and edge detection of Sobel operator,and the text position detection of terminal row connecting line text area based on the horizontal and vertical projection segmentation algorithm and direction rotation are proposed.The semantic extraction test on 30 marked drawings is conducted by the proposed method,the average F1 value of the test set is 91.25%,and the average intersection over union mean of the test set is 82.61%,which verifies the effectiveness and robustness of the proposed algorithm.