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The accurate and rapid detection and recognition of jet features are key to dynamic monitoring and online control of the electrospinning process. In this study, a real-time recognition system based on OpenCV was introduced into a coaxial laser-assisted electrospinning system to solve the difficulties of accurate jet recognition and to promote an image processing algorithm response. The jet images with laser assistance were more clearly visible than those without laser assistance, and a significant contrast in grayscale levels existed in the jet image to help distinguish jet features. Subsequently, separate algorithms were designed for the jet visible length calculation, and the recognized visible length of the jet and algorithm running speed were compared. The average visible length of the jet with laser assistance was 11.49 mm, which increased by 1.59 mm compared to that without laser assistance. In addition, the running time of the algorithm with laser assistance was 24.89 ms, reduced by 14.84 ms compared to that without laser assistance, indicating the effectiveness of laser assistance to promote the accuracy and running speed of the jet image recognition process. Additionally, real-time detection of the jet angles was achieved to identify instances of excessive deflection during the electrospinning process. Overall, this study has significant potential to promote the dynamic monitoring of an electrospinning jet. [ABSTRACT FROM AUTHOR]

The stayed-cable is an important component of cable-stayed bridges, with cable force being a focal point during construction and bridge operation. The advancement of camera and image processing technology has facilitated the integration of computer vision technology in structural inspection and monitoring. This paper focuses on enhancing cable force measurement methods and addressing the limitations of traditional testing techniques by conducting experimental research on cable force estimation using video recording. The proposed approach involves capturing video footage of the target on the cable with a smartphone. Subsequently, a combination of techniques such as the background subtraction method, image morphology processing, and Hough transform image processing technology are employed to detect the precise center coordinates and ultimately obtain the accurate displacement–time curve of the cable's vibration. In addition, the graphic Circularity Coefficient (CC) has been introduced to assess its effectiveness in post-motion-blur image processing for circular targets. The fundamental frequency of the cable is determined by the fast Fourier transformation, and the relationship between the cable force and the fundamental frequency is used to estimate the cable force. The experimental results are compared with data from accelerometers and force gauges, demonstrating that the frequency measurement error is below 1.2% and the cable force test error is less than 3%. In the process of acquiring the cable's fundamental frequency, the test directly employs the pixel as the displacement unit, eliminating the need for image calibration. The innovative use of the CC in processing motion-blurred targets ensured accurate recognition of target coordinates. The experimental findings highlight the method's simplicity, speed, and accuracy. [ABSTRACT FROM AUTHOR]

Currently, most buildings are constructed using prefabricated concrete slabs supported by a steel skeleton that is generally tied and welded manually. However, if the overall size of the skeleton is incorrect and this error is not noticed before the concrete is poured, then a huge waste is incurred by having to scrap all the prefabricated slabs. Therefore, we propose an automatic system for measuring the frame size needed for prefabricated slabs made of reinforced concrete using LabVIEW and the NI Vision library of machine-vision functions to perform image processing and machine-vision detection. A charge-coupled device camera obtained an overall image, and then, LabVIEW processed the image and extracted its features. The system compared the measured size with the design size and issued a warning if the two differ by more than 0.5 cm. From experimental comparison and verification, the maximum measurement error between the test results and the actual size was 2.7% and the root mean square error was 0.66%. This meets the requirements of actual site construction and provides a scientific reference for realizing the automatic detection of the skeleton size for reinforced-concrete precast slabs. [ABSTRACT FROM AUTHOR]