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This study aims to develop a high-speed and nondestructive mildewed rice grain detection method. First, a set of microscopic images of rice grains contaminated by <i>Aspergillus niger</i>, <i>Penicillium citrinum</i>, and <i>Aspergillus cinerea</i> are acquired to serve as samples, and the mildewed regions are marked. Then, three YOLO-v5 models for identifying regions of rice grain with contamination of <i>Aspergillus niger</i>, <i>Penicillium citrinum</i>, and <i>Aspergillus cinerea</i> in microscopic images are established. Finally, the relationship between the proportion of mildewed regions and the total number of colonies is analyzed. The results show that the proposed YOLO-v5 models achieve accuracy levels of 89.26%, 91.15%, and 90.19% when detecting mildewed regions with contamination of <i>Aspergillus niger</i>, <i>Penicillium citrinum</i>, and <i>Aspergillus cinerea</i> in the microscopic images of the verification set. The proportion of the mildewed region area of rice grain with contamination of <i>Aspergillus niger</i>/<i>Penicillium citrinum</i>/<i>Aspergillus cinerea</i> is logarithmically correlated with the logarithm of the total number of colonies (<i>TVC</i>). The corresponding determination coefficients are 0.7466, 0.7587, and 0.8148, respectively. This study provides a reference for future research on high-speed mildewed rice grain detection methods based on MCV technology.