Edge-Enhanced Matching for Gradient-Based Computer Vision Displacement Measurement

Computer vision-based displacement measurement for structural monitoring has grown popular. However, tracking natural low-contrast targets in low-illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity-based vision-sensing techniques. A new edge-enhanced-matching (EEM) technique improved from the previous orientation-code-matching (OCM) technique is proposed to enable robust tracking of low-contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked-segmentation filtering technique is also developed to post-process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low-contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long-span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy.