Abstract: To achieve accurate real-time detection of vibration parameters of vibrating screening equipment, an in-situ measurement system based on machine vision was designed. The system integrated a ring light source, infrared filter, and autofocus module, and employed a high-speed area array industrial camera for high-resolution image acquisition, improving the imaging quality of the detection system in low-light and low-contrast environments. Adoption of calibration algorithm based on corner detection improved the efficiency of the camera calibration process. A multi-scale image process enabled sub-pixel localization of feature marker extraction at a low computational complexity. A single-cycle curve fitting method based on spline interpolation was proposed for fast real-time vibration parameter acquisition and data processing. The results showed that: 1) With measuring distance less than 0.8 m, the system achieved a scale factor lower than 0.12 mm/px. 2) The proposed calibration, feature point extraction and curve fitting algorithms improved computational efficiency by over 50% compared to conventional methods. 3) The proposed single period curve fitting method based on spline interpolation had a maximum error of 0.06 mm for peak-to-peak values and 0.35 r/min for frequency, representing reductions of 33.33% and 58.82% respectively compared with those of multiple period curve fitting methods, meeting the performance requirements of vibration parameter detection instruments specified in the tobacco industry standards. This system enables accurate online detection of the vibration parameters for vibration screening equipment, meeting the in-situ detection requirements of vibration parameters for cut tobacco and tobacco strip size.