Self-adaptive bridge bare rebar detection algorithm based on local image segmentation and multi-feature filtering

Due to the interference of the uneven illumination and the complex background, the traditional threshold segmentation methods are difficult to effectively separate the bare rebar from the background. An adaptive bridge bare rebar detection algorithm based on local image segmentation and multi-feature filtering was proposed. Firstly, the gray value of the gray image was needed to project, and the wave trough and its coordinates formed by the bare rebar on the projection were found out. Secondly, with the wave trough coordinates as the center, the segmentation range was set to divide the rows and columns of the gray image, and the merged gray image was segmented by the local threshold. Finally, the extraction of the bare rebar feature was realized based on the multi-feature filtering, and the proposed algorithm was used to verify 7 kinds of common bare rebars. The experimental results show that the average error rate, the missing rate and the relative error of the bare rebar length with manual measurement are 5.15%, 3.89% and 3.74%, respectively, which meet the criterion of the highway disease evaluation and realize the adaptive recognition of the bare rebar under complex environment.