This paper presents a full research and development cycle of a vision-based system for long-term remote monitoring of civil engineering constructions. To measure displacements, the system analyzes reference mark position using a developed image processing algorithm. All computational operations are performed by FPGA to ensure high performance. The developed monitoring system has an accuracy of 0.45 mm (3 standard deviations) at the distance up to 50 m. The measurement error remains within this value even in harsh environments. The value of this paper is formed by the possibility of stable 24/7 monitoring in low-temperature conditions (up to C).

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1. The objective of this study was to develop a machine vision method for analysing exterior parameters of chicken eggs to automate the stage of primary sorting. 2. The developed algorithm based on predetermined thresholds calculated egg quality indicators, including geometric dimensions, shape index and the mottling grade. The algorithm was implemented with an experimental setup that combined the image-based and the candling methods. A total of 400 egg samples were analysed. 3. Comparison of results of the algorithm with those obtained using the traditional manual method showed that mean value of radii values difference was 0.095 ± 0.058 mm for the sharp and 0.080 ± 0.047 mm for the blunt end of the egg, with standard deviations of 0.58 mm and 0.49 mm, respectively. 4. The correlation coefficient between the shape index values determined by the two methods was 0.93; the standard deviation of absolute differences between corresponding values was 1.05%. 5. The results of mottling grade estimation were compared using F-measure and confusion matrix. 6. The results allow the possibility to perform the assessment of egg exterior quality factors in an automatic mode, independent of the expertise of a grader.

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