A machine vision based surface defect detection method for microchannel flow channel plates is proposed to meet the demand for automatic detection of complex surface defects in industrial automation production. This method focuses on common pit and damage defects in the CV holes and expansion valve holes of the flow channel plate. Firstly, the ROI region is extracted through Hoff circle detection to eliminate background interference. Gaussian filtering is used to filter the ROI image, and binarization and morphological corrosion operations are used to filter out interference noise to highlight defect features. Then, the Two-Pass algorithm and seed filling method are used to calculate the connected domain to achieve pit defect detection. Use circle search to find the inner and outer circles of the hole end surface, unfold the circular ring, and use Canny edge detection operator to search for the defect contour, screen the contour area to achieve the detection of damaged defects. Through comparative experiments, it has been verified that the method proposed in this paper has a higher detection rate in the detection of defect samples in the runner plate compared to traditional surface defect detection methods. The method proposed in this article has been validated to have a stable defect detection rate of over 92% on the surface of the flow channel plate, and the algorithm has fast processing speed and strong robustness, achieving fast, non-contact high-precision detection and meeting the requirements of industrial automation.