Aiming at the characteristics of the angle steel punching circle defect, this paper proposes a method for detecting the punching circle defect based on the genetic algorithm to optimize the BP neural network. Using BP neural network to detect the defects of the punching circle can effectively solve the problem of nonlinear mapping between the input and output of the punching circle defect. The traditional BP neural network model is easy to fall into the local minimum and cause the risk of model failure. The genetic algorithm is used to optimize the weights and thresholds of the BP neural network, and the obtained optimal weights and thresholds are substituted into the prediction model for defects. Detection can improve the stability and predictive ability of the model. Experiments show that the GA-BP network has higher accuracy and generalization ability than the unoptimized BP network, and can accurately detect the punching circle defects of the power tower angle steel.
Aiming at the current courier companies' security inspection coding generally adopts manual coding operation, the coding speed is low, and the coding quality is guaranteed, a fast coding device based on color characteristics and rapid segmentation by K-method is designed. System, and image processing are composed of two image systems. The image processing system is used to obtain the coordinates of the express location, and the machine executes the operation of fast and high-speed coding. The express positioning algorithm first performs image color feature processing on the collected express, performs Gaussian filtering of noise, uses K-means to shoot the target, performs critical value segmentation of the target, morphological processing and minimum contour detection to extract the diagnosis center coordinates, and finally the extracted coordinates are passed to the mechanical result agency for coding. Guarantee: Using the inkjet code designed in this article to carry out the courier security inspection coding experiment, the accuracy rate reaches 98%, which greatly improves the express security inspection device.
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