Ice scoop surface defects include severe defects and minor defects. Serious defect detection technology is relatively mature, while mild defect detection involves the game of missed detection and false detection. Serious defects belong to the waste products that cannot be sold. Splitting and pollution in the mild defects are also waste products, while knots, mineral threads, decay, and color difference belong to qualified products, and the price is lower than that of good products without defects. This article proposes a set of solutions for the detection of mild defects: Firstly, the contour of the ice scoop is extracted and the surface area of the ice scoop is located. Then, the surface of the ice scoop is preprocess. Finally, the linear equation fitted by the least-squares method and the preprocessed gray value data subtract are used to calculate the deviation. The normal surface deviation value is obtained by iterative fitting of the least-squares method, thereby calculating its standard deviation, and finally extracting the abnormal data (defect) based on the 3σ criterion. The above algorithm can detect small-area light defects. For large-area decay, chromatic aberration, and other defects, the deviation calculation based on the gray peak level linear equation is adopted. A combination of the two methods detects surface defects on ice scoops. The ice scoop surface defects studied in this paper cover the main types of surface defects in the ice scoop production industry, including knots, mineral lines, splitting, contamination, decay, and chromatic aberration. The algorithm test on the self-built image database shows that the missed detection rate of this method is only 1. 27% , and the false detection rate is reduced to 3. 85% , which demonstrates its practical deployment value.