Aiming at the problems of poor detection ability of existing track fastenings state detection algorithms on small target objects and complex shapes,which lead to abnormal detection results,as well as feature redundancy of small target layer,an improved YOLOv8 track fastenings state detection method was proposed.A deformable space pyramid expansion convolutional module is added in YOLOv8 network to improve the detection accuracy of small target objects and deformable complex objects.At the same time,the reconstruction unit of small target space is added to reduce the redundancy of small target features and promote the learning of small target features.The model is trained and tested according to the collected data set of track fasteners,and compared with multiple groups of track fasteners state detection algorithms.The experimental results show that compared with the comparison algorithm,the accuracy of the proposed algorithm is increased by 3.20%on average,the recall rate is increased by 3.34%on average,and the average accuracy is increased by 3.96%on average.The experiments proves that the proposed algorithm can detect the state of rail fastenings effectively,and has strong generalization ability,and can be deployed in complex traffic scenarios.