Space-time image velocimetry is a one-dimensional time-averaged flow velocity measurement method characterized by high spatial resolution and real-time performance. However, it is susceptible to gross errors in complex scenarios and requires manual parameter tuning, limiting its environmental adaptability. To overcome this, this paper proposes a fused method combining texture orientation detection and discrimination. Based on texture enhancement and frequency domain transformation, image segmentation is used to separate valid and invalid signals in the spectrum. While detecting texture angles, the local features of the segmented signal are used for statistical discrimination, thereby reducing noise interference from erroneous angles. Parameter determination, sensitivity analysis, multi-scene comparison, flow rate ratio measurement and calibration test experiments are also carried out. Results show that with parameters optimized via large-sample statistics, the proposed method reduces mean absolute error by 58.32%, 42.94% and 29.66% compared to frequency-domain velocimetry using three different integration radii. Root mean square error is reduced by 36.90%, 22.60% and 13.56%, respectively. In velocimetry measurements at Panzhihua and Maozhouhe stations, the relative error remained within 7.88%. Calibration at Panzhihua showed a systematic error of 0.188% and random uncertainty of 4.879% in cross-sectional flow. The sign test, line fit test, and deviation test all passed, confirming the method′s accuracy and robustness in complex flow scenarios.