Abstract
Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm.
Highlights
In the application field of image classification and recognition, BP neural network shows great development potential and prospect. e use of intelligent algorithms based on BP neural network to classify and recognize images provides a new direction and new thinking for the recognition method, which helps image classification be widely used in various fields
From the training convergence graph of the particle swarm optimization (PSO)-BP, it can be seen that the PSO-BP algorithm converges at 78 iterations, which improves the performance and efficiency compared to the BP neural network
10 20 30 40 50 60 70 80 90 Epochs to prevent the zero gradient problem. en, the optimized VGG-16 model was subjected to image classification experiments, and the results proved that the optimized model has improved the classification performance
Summary
Machine vision technology is widely used in the field of image processing and classification. People began to study new image processing technology and applied neural networks to the field of recognition. Image processing algorithm is an image processing technology and a research hotspot. Artificial neural network (ANN) is an information processing method that simulates human brain neurons. E human brain can process complex information quickly and in parallel, in different environments, and image information can be effectively processed. Robust adaptive image processing can deal with nonlinear noise or impurity data in the image
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