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

Abstract

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.

Keywords

• region segmentation
• text detection
• small target detection
• yolov5
• paddleocr