To assist maintenance personnel in detecting the fastening status of railway locomotive bogie bolts through visual image analysis, a railway locomotive bogie bolt fastening status detection method based on image recognition is proposed. Firstly, the YOLOv7 algorithm is used to quickly locate bolts in the image, and the strong robustness and generalization ability of deep learning algorithm are utilized to accurately obtain bolt target detection results images including bolts and their positioning paint in various scenarios of maintenance. Secondly, the bolt target detection result image is converted into YCbCr space, combined with the color characteristics of the bolt positioning paint, the Cr component image is extracted, and an adaptive segmentation algorithm is applied to effectively filter out background pixels to obtain a binary image containing only the bolt positioning paint. Finally, based on the differences in shape, position, and angle of bolt positioning paint, Hu moment features were extracted as quantitative representations of bolt positioning paint status information, and a classification model was established using SVM to obtain the final bolt tightening status detection results. The experimental results show that this method fully utilizes the characteristics of railway locomotive bogie bolts. While ensuring the accuracy of bolt target detection and bolt positioning paint segmentation, the accuracy of bolt tightening status in railway locomotive bogie bolts in all scenarios is 92. 42%, the recall rate is 94. 55%, and the average accuracy rate is 93. 28%.