A state detection method of track fasteners based on YOLO is proposed.In order to improve the detection speed

the convolution layer of YOLO neural network is optimized and back propagation algorithm is used to update weighting parameter of loss function.According to VOC dataset format

sample set is created as well as trained and tested the model on the dataset.The comprehensive test results show that the accuracy of YOLO detection algorithm is 94.2 %

the recall rate is 85.1 %

and the detection speed is up to 60 f/s

which has good robustness to the environment

using this method and the other two algorithms to compare and analyze.It shows that the model has the fastest detection speed.