基于经典机器学习的墙柱施工图图像识别

doi:10.12066/j.issn.1007-2861.2196

Abstract

Abstract:

Drawing review is important in the engineering design field, and efficient and accurate drawing review is a direction for future development. In recent years, the field of artificial intelligence has developed rapidly. This paper proposes an image recognition method based on classic machine learning that aims to use a computer to identify drawings efficiently and improve the efficiency of drawing review. First, the $K$-means algorithm is used to cluster wall column construction drawings, and based on the clustering, the local boundaries are searched, and the characters are divided. Subsequently, the $k$-nearest neighbour (kNN) model is used to identify the characters, and the reinforcement information of the wall column construction drawings is obtained. In addition, this paper proposes the Shadow-Sum method, an algorithm that can efficiently calculate the wall column area in shear wall construction drawings. Experiments are performed by randomly taking two wall column construction drawings. The results reveal that the accuracy of character recognition for the two wall column construction drawings is 99.6% and 99.4%. The recognition error rates for the areas are 0.66% and 0.34%. Column construction drawing identification is beneficial because the main information in wall column construction drawings can be output as editable text, which has certain engineering application value.

Key words: image recognition, wall column construction drawing, machine learning

CLC Number:

TU206

TAO Li, ZHU Jiejiang, CAI Honghao. Classic machine learning for image recognition in wall column construction drawings[J]. Journal of Shanghai University（Natural Science Edition）, 2021, 27(5): 940-949.

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References 11

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Classic machine learning for image recognition in wall column construction drawings

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