The contamination proposed in this paper is a defect on the surface of ice cream bar, which is a serious security threat. So it is essential to detect this defect before launched on the market. A detection method of contamination defect on the ice cream bar surface is proposed, which is based on fuzzy rule and absolute neighborhood feature. Firstly, the ice cream bar surface is divided into several sub-regions via the defined adjacent gray level clustering method. Then the alternative contamination regions are extracted from the sub-regions via the defined fuzzy rule. At last, the real contamination regions are recognized via the relationship between absolute neighborhood gray feature and default threshold. The algorithm was tested in the self-built image database SUT-D. The results show that the accuracy of the method proposed in this paper is 97.32 percent, which increases 2.68 percent at least comparing to the other typical algorithms. It indicates that the superiority proposed in this paper, which is of actual use value.