钢轨内部损伤危害严重，但无损探伤B显检测图像杂波噪声多、不同损伤时空分布特征不明显，难以有效识别。针对这一情况，提出了一种基于改进YOLOv8的钢轨螺孔裂纹B显图识别算法，从提高钢轨损伤智能识别精确率。首先，为减少小伤损目标漏检，采用RepHGNetv2网络优化YOLOv8主干网络，提高检测召回率；然后，为提升模型对不同类型损伤检测的适应性，将YOLOv8的检测头替换为Effcientnet，提高模型检测精度；最后，在SPPF模块中引入LSKA注意力机制，提升模型对噪声信号的抗干扰能力，提高模型精确率。经实际线路检测结果验证，上述模型检测精确率达到95.1%，召回率达到93.8%，平均精度达到97.6%，对比其他常用算法均有所提高。

The internal damage of the steel rail is serious, but the non-destructive testing B-display detection image has a lot of noise and noise, and the spatiotemporal distribution characteristics of different damages are not obvious, making it difficult to effectively identify. In response to this situation, a rail screw hole crack B-image recognition algorithm based on improved YOLOv8 was studied to improve the accuracy of intelligent identification of rail damage. Firstly, to reduce the missed detection of small damage targets, RepHGNetv2 network was used to optimize the YOLOv8 backbone network and improve the detection recall rate. Then, in order to improve the adaptability of the model to different types of damage detection, the detection head of YOLOv8 was replaced with Effientnet to improve the detection accuracy of the model. Finally, the LSKA attention mechanism was introduced into the SPPF module to enhance the model's anti-interference ability against noise signals and improve its accuracy. The actual line detection results have verified that the detection accuracy of the above model reaches 95.1%, the recall rate reaches 93.8%, and the average accuracy reaches 97.6%, which is improved compared to other commonly used algorithms.