Abstract
A computer-aided automatic safety evaluation method is proposed based on quality evaluation on digital images of roads or bridges and other image information collected by highway monitoring devices. Images of qualified roads or bridges are selected to form a reference image database, and reference image sequence and evaluation image sequence are established separately. Then combined with the peak signal to noise ratio (PSNR) and the human visual characteristic information entropy, a safety evaluation function with dynamic weights is obtained. At last, the evaluating algorithm is used to compare similarities between evaluation images and reference images to judge the quality of roads or bridges and get a sequence of evaluation parameters sequence. If the value of the evaluation parameter is greater than the threshold, the road or bridge quality changes greatly, and therefore artificial inspection is required. The experimental results show that the evaluation is consistent with the subjective perception of human vision, and the method proposed in this paper has high degree of automation.
Highlights
As photography, remote transmission, and storage technologies develop, the digital image processing technology are combined more closely with road engineering and bridge monitoring [1], in crack recognition and license plate recognition and positioning
Researchers tend to study the image quality evaluation based on human visual features [4,5,6], but the computation is complicated when being applied in evaluating images with complicated texture
peak signal to noise ratio (PSNR) is combined with the information entropy, a human visual parameter, to serve as a measure index, and a correlation comparison method is put forward based on human visual characteristics
Summary
Remote transmission, and storage technologies develop, the digital image processing technology are combined more closely with road engineering and bridge monitoring [1], in crack recognition and license plate recognition and positioning. Researchers tend to study the image quality evaluation based on human visual features [4,5,6], but the computation is complicated when being applied in evaluating images with complicated texture In this regard, researchers put forward a variety of quality measurement methods. The MSSIM method [7] evaluates similarities between a distorted image and a reference image from luminance, contrast ratio, and structure. IFC [8] and VIF [9] methods evaluate quality by calculating correlation of mutual information between distorted and reference images. Both methods have good performance, but wavelet decomposition brings complex calculation. The image quality evaluation principle is used to conduct auxiliary safety assessment for roads or bridges and form a time quality evaluation sequence, providing data for effective maintenance management and performance prediction
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