In the double-sided shearing process, the steel plate alignment process requires manual visual observation of the laser beam allowance, which is complex in operation and subjective judgments that affect data accuracy. Therefore, in this paper, an automatic alignment system for double-sided steel plate shearing based on machine vision is designed, which relies on multiple sets of area array cameras distributed along the roller table to collect the status data of the steel plate on the roller table. Using on-site measurement data and the target width of the steel plate, two virtual cutting lines are calibrated, eliminating the dependence on traditional auxiliary laser lines. At the same time, a cascaded steel plate object extraction model is adopted in the system, and the step-by-step extraction idea of “rough first and then fine” is adopted to improve the accuracy of steel plate edge detection. The movement distance is converted based on the relationship between the steel plate contour position and the virtual shear line position, thereby controlling the magnetic centering device to complete the steel plate centering process and improving the automation of the double sided shear process. The actual application results show that the system has a measurement error of less than 5 mm for the width of steel plates, and an automatic control centering error of less than 10 mm, meeting the automatic control requirements of enterprises.