The crack is a serious defect on the ice cream stick surface, which affects the processing and usage of the ice cream stick seriously. However, certain thin and light cracks are similar to the wood fiber textures on the ice cream stick surface, which results in the poor extraction performance of present detection algorithms. Aiming at this problem, a detection scheme based on the combination of the mainlobe and sidelobe texture gray features is proposed on the basis of detailed analysis of crack texture characteristic and wood fiber texture characteristic. Firstly, the basic model of the texture mainlobe and sidelobe gray characteristic extraction is built. Secondly, the whole texture edges on the ice cream stick head are extracted. Then, the mainlobe and sidelobe texture gray features of corresponding textures of the edges extracted in the previous step are extracted according to the established model, and the candidate crack edges (containing all the crack edges and certain wood fiber texture edges) are preliminarily determined according to the mainlobe characteristic quantity size. Lastly, the crack texture edges are recognized from the candidate edges derived from the previous step according to the numerical relationship of the sidelobe characteristic quantity and the preset threshold value; and the crack defect detection is achieved. The algorithm was tested on the selfbuilt image database SUTI2. The results show that the crack defect FAR of this method is as low as 6.07 percent on the premise that the missing detection rate is 0; the missing detection rate is decreased by at least 9.29 percent compared with other ice cream stick or wood fiber surface crack detection algorithms, which indicates that the proposed crack detection scheme is superior and has actual application value.