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Aiming at the problems of low efficiency and unstable precision of manual pearl shape sorting, a pearl shape detection method based on machine vision is proposed. The backlight imaging method is adopted to eliminate the influence of pearl surface texture and luster, and pre-processing algorithms such as homomorphic filtering are performed on the acquired pearl image to improve the image contrast. In order to solve the problem that the contacting pearls affect the extraction of the pearl contour, the watershed algorithm is used to segment the pearl image, and the independent pearl individual is obtained, and then the pearl is located by the connected domain mark and the centroid algorithm. According to the national standards on pearl shape, the pearl shape parameter model is established based on the pearl image information to quantify the pearl shape. Experimental results show that the detection error of pearl samples of different shapes is 0.63%, the shape statistics accuracy is 100%, and the algorithm takes 24 ms. This method can sort and classify pearls accurately and efficiently, and has certain practical value.