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In order to decrease the noise enhancement in the process of phase unwrapping, we present a new phase unwrapping approach for multiple wavelength phase shift profilometry. In this approach, a series of fringe images with only three wavelengths are captured by the 3D measurement system, and it does not need to calculate the equivalent wavelengths and the equivalent phases. As a result, the noise enhancement is avoided, leading to improved precision in the absolute phase map recovery. Experiment results demonstrate that the proposed method is more robust and effective compared to the existing TWPSP method.

In order to realize the online measurement of lamp dimension, the bulb image dimension measurement based on vision (BIDMV) is proposed. The image of lamp is obtained by camera. After image processing, such as Otsu algorithm, median filter, ellipse fitting and envelope rectangle fitting, the dimension of lamp can be calculated. Based on this method, a non-contact real-time measurement system of the lamp’s dimension is developed. The precision of the proposed method is 0.07 mm, and it can satisfy the tolerance of the National Standard GB15766.1-2008. The experiment results show that the proposed method has a faster measuring speed and a higher precision compared with other measurement methods.

To achieve accurate measurements, the creating a fitting hole for internal diameter (CFHID) measurement method and the establishing multi-sectional curve for external diameter (EMCED) measurement method are proposed in this paper, which are based on computer vision principle and three-dimensional (3D) reconstruction. The methods are able to highlight the 3D characteristics of the scanned object and to achieve the accurate measurement of 3D data. It can create favorable conditions for realizing the reverse design and 3D reconstruction of scanned object. These methods can also be applied to dangerous work environment or the occasion that traditional contact measurement can not meet the demands, and they can improve the security in measurement.

A high-precision vision detection and measurement system using mobile robot is established for the industry field detection of motorcycle frame hole and its diameter measurement. The robot path planning method is researched, and the non-contact measurement method with high precision based on visual digital image edge extraction and hole spatial circle fitting is presented. The Canny operator is used to extract the edge of captured image, the Lagrange interpolation algorithm is utilized to determine the missing image edge points and calculate the centroid, and the least squares fitting method is adopted to fit the image edge points. Experimental results show that the system can be used for the high-precision real-time measurement of hole on motorcycle frame. The absolute standard deviation of the proposed method is 0.026 7 mm. The proposed method can not only improve the measurement speed and precision, but also reduce the measurement error.

For fabric defect identification in the textile industry, a three-dimensional (3D) color phase shift profilometry (CPSP) method is proposed. The detecting system is mainly composed of one CCD camera and one digital-light-processing (DLP) projector. Before detection, the system should be calibrated to make sure the camera parameters. The CPSP color grating is projected to the measured fabric by DLP projector, and then it is collected by CCD camera to obtain the grating phase. The 3D measurement can be completed by the grating phase difference. In image acquisition, only invariable grating is projected to the object. In order to eliminate the interference from background light during the image acquisition, the brightness correction method is researched for improving the detection accuracy. The experimental results show that the false rate of detecting the fabric defects is 5.78%, the correct rates of detecting the fabric defects of hole and qualified fabric are both 100%, and the correct rates of detecting the fabric defect of scratch and fold are 98% and 96%, respectively. The experiment proves that the proposed method can accurately identify fabric defects.