Since deformation estimation may lead to errors occurring when the camera vibrates, it is necessary to remove the image global motion before computing real bridge deformation. In this study, a combination of image motion correction algorithm and 2D image-based deformation measurement technique was utilized to address the issue of camera motion during the image data acquisition for bridge deformation measurement. Based on the proposed methodology, the image motion parameters were estimated by defining an effective sub-image in the image and using Iterative Affine Motion Estimator. Then the estimated parameters were applied to all pixels of each captured image to remove the motion effect. Finally, the corrected images were used to analyze by a 2D image-based deformation measurement technique in order to extract and measure real bridge deformation by tracking artificial or natural targets. The proposed methodology was validated by two experiments in the lab and field environments. Achieved results show the accuracy and reliability of the proposed methodology.

This paper presents an evolutionary algorithm for recognizing high and low crash road segments using Genetic Algorithm as a dynamic segmentation method. Social and economic costs as well as physical and mental injuries make the governments perceiving to road safety indexes in order to diminish the consequences of road accidents. Due to the limitation of budget for safety improvement of all parts of the road, the road segments with more accidents should be recognized for safety budget assignment. So, considering this fact it's important to identify the segments with high and low number of accidents to optimize the road safety program. In this study, a novel chromosome coding method and a fitness function which are consistent with Genetic Algorithm are proposed. The proposed methodology is also validated by using two mathematical parameters so that the results confirm that the proposed modeling works properly. Afterward, the proposed dynamic segmentation method is compared with the other static segmentation methods along 51 km of Shahrood–Sabzevar highway. The proposed method may have more advantages comparing to static segmentation methods for all of the performance indexes which were considered in this study. The proposed method has a variance about two times higher than the one for accident density in comparison with the other static segmentation methods. About 62% and 34% improvement is achieved in average of segments accident density and total segments density respectively in comparison with the other fixed methods.

A non-contact optical measurement system using a sub-pixel motion correction and target tracking algorithm is proposed in order to remove the probable camera vibration during the measurement test and calculate the real bridge deflection. To achieve this goal, an image correction algorithm is utilized to estimate and correct the global motion of the camera. Subsequently, computer vision and image processing algorithms are used to measure the deformation of the bridge structure. Two experimental studies are conducted and presented to validate the applicability and accuracy of the proposed methodology in the laboratory and in the field. The results showed that this approach can be utilized accurately as a step toward solving the problem of structural deflection measurement with considering camera motion.