Research on Crack Image Recognition and Progressive Automatic Annotation Method Based on Mask RCNN
This paper presents a progressive fully automatic annotation algorithm,which annotates crack samples in three stages.Firstly,it draws fake cracks by drawing lines on white paper,and extracts the crack contours to make labels,and then trains to generate the first-level weight file.Secondly,it uses the first-level weight file to identify the cracks on the white wall,and optimizes the mask to make labels,and then trains to generate the second-level weight file.Finally,it utilizes the second-level weight file to conduct batch detection of concrete cracks,optimizes and extracts the mask contours to generate labels,and then trains cyclically to produce the third-level weight file.After training,the comprehensive evaluation indicators of the Mask RCNN model for the recognition of three types of images are 95.2%,83.3%,and 79.2%,respectively.The detection rate is relatively high and this model can be applied to the rapid recognition of cracks.
国家科技期刊平台
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