In order to solve the problem of image distortion of defect magnetic flux leakage (MFL) signal caused by unsaturated magnetization of small pipe diameter elbow, and realize intelligent and high-precision identification of elbow distortion magnetic flux leakage defect image. This paper proposed an intelligent image identification method for distortion MFL defects in small-diameter pipe elbows. The MSRCR-HF image restoration algorithm was applied to process the distorted images, which to solve the problem of defective image distortion caused by the weak MFL signal of the elbow. The YOLOv5 network was optimized by integrating the CBAM and the SPD-Conv module to improve the network's feature extraction ability for elbow distortion and MFL defects. Finally, the elbow defect datasets were established through simulation, and it was input into the network for training and testing. The results shown that the MFL signal image of the same defect at the elbow was distorted, and the defect feature information cannot be directly and effectively obtained. The proposed MSRCR-HF algorithm effectively resolved the image distortion problem associated with elbow MFL defects. Additionally, the improved YOLOv5 model achieved high recognition accuracy on the established dataset, with accuracy rates of 95.5% for rectangular groove defects, and 93.0% for hemispherical defects. This method exhibited strong feasibility for intelligent identification of distortion MFL defect in small-diameter pope elbows and can improve the efficiency of pipeline safety inspection.