Manual failure detection in the train of electric multiple unit (EMU) failure detection system (TEDS) involves high error rates and significant workload. To address this problem, this paper proposes an SSD-YOLO algorithm for detection of EMU brake disc bolts failures, which is improved from YOLOv5n. The network structure of YOLOv5n is improved considering that the failure areas of brake disc bolts are small and the failures are similar to that of normal samples. To make the model flexibly adapt to the features of different scales and enlarge the receptive field, the down sampling convolution layers in the backbone network are replaced with switchable atrous convolution layers. To enhance the ability of the network to obtain global information and interact with contextual information, this study integrates the Swin-Transformer module at the end of the backbone network. To better deal with semantic information of different scales and resolutions, the coupled detection head of the YOLO series is replaced with an efficient decoupled head, which can extract target location and category information. To further improve the training convergence speed of the algorithm, the SCYLLA-intersection over union (SIoU) loss function, which has better positioning ability, is used to replace the CIoU loss function. In this study, artificially annotated samples of the EMU brake disc bolts are used to train the network. Experiments show that the detection mean average precision (mAP) value of the improved algorithm on the EMU brake disc bolts failure dataset increased by 6.8 percentage points to 98.3%, compared with 91.5% of the original YOLOv5n model. A detection frame rate of 89 frame/s is achieved on the RTX3090 graphics card, which is 1.7 times that of YOLOv5l and 3.7 times that of YOLOX-S, meeting the real-time requirement of TEDS failure detection. The SSD-YOLO algorithm can quickly detect the missing failures of the EMU brake disc bolts, reduces the manual workload of analysts, and provides a reference for future research on condition maintenance of EMU.