Aiming at the complex anatomical structure of the spine, a YOLOv4-disc algorithm for spinal magnetic resonance imaging image detection is proposed. First, aiming at the problem of small number of real case samples, the adaptive histogram equalization (CLAHE) data enhancement method with limited contrast is used to improve the generalization ability of the model. Second, K-means algorithm is used to cluster the size of real frames in the dataset to obtain the appropriate anchor frame size and determine the number of anchor frames. After that, depth separable convolution is used in CSPDarknet-53 backbone feature extraction network instead of ordinary convolution to reduce network parameters and reduce computation. Finally, the loss function of the native network is improved based on Focal loss to solve the problem that the proportion of positive and negative samples is seriously unbalanced in one-stage target detection. The experimental results show that the mean average precision (mAP) of the proposed YOLOv4-disc algorithm reaches 90.80%, which is 3.51 percentage points higher than that of the native YOLOv4 algorithm.