Improved BP network algorithm´s application of textile monitoring image recognition

Glass fiber textile machine is a major producer machine of glass fiber cloth. Textile machines weft detection usually uses the contact type in production, requires that the weft maintains certain pressure to the sensor. This way will cause glass fiber weft bristling, and will produce glass fiber floating dust. Damage to the textile machine and has the harm to the human body health. We use video surveillance method to detection the weft, image recognition and speed directly affects the stability of the system. In the BP neural network model to increase the momentum factor, combined with self-learning dynamic adjustment of parameters of hidden layers. Using BP neural network algorithms, avoid network into a local minimum, increase system speed and stability, and achieved better control effect.