Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay

This paper aims to eradicate the poor performance of the convolutional neural network (CNN) for intelligent analysis and detection in samples. Moreover, to avoid overfitting of the CNN model during the training process, an algorithm is proposed for the fusion of maximum pooled and weight decay. Firstly, the maximum pooled method for the pooling layer is explored after mask processing to reduce the number of irrelevant neurons. Secondly, when updating the neuron weight parameters, the weight decay is introduced to further cut down complexity in model training. The experimental comparison shows that the overfitting avoidance algorithm can reduce the detection error rate by more than 10% in image detection than other methods, and it has better generalisation.