The background difference method and cross-correlation method used in the extraction of the bullet position in the images collected by the traditional CCD intersection stand-up target have the problems of poor versatility and long time-consuming. By analyzing the problems existing in CCD precision target image projectile extraction, a method for CCD precision target image projectile extraction based on IGWO algorithm is proposed. The dimensional learning-based hunting (DLH) search strategy is used to update the position of each search factor through the neighborhood. Generate candid[ ]ate solutions, increase the diversity of search populations, and jump out of local optimal solutions. The bullet extraction problem is transformed into the problem of finding the minimum connected region of gray value under certain constraints. The minimization area gray value model, the vertical light spot area and the low gray area elimination model were established. Under the same parameter setting, the IGWO, GWO, MFO algorithm, cross-correlation algorithm, and background difference method were used to conduct comparative experiments. The experimental results show that the target detection success rate of the IGWO algorithm is much higher than other algorithms, reaching 95%, and the algorithm solution time is much lower than other algorithms, shortening to 12ms.