基于改进的卷积神经网络的道路井盖缺陷检测研究

Research On Manhole Cover Detection Using Improved Convolutional Neural Network

摘要:

道路井盖缺陷检测对于道路维护与安全至关重要,论文提出了一种改进的卷积神经网络算法,可实现井盖缺陷的快速、准确检测。算法对卷积神经网络的激活函数模型进行了改进,针对Relu激活函数在输入小于零时输出设为零,导致部分缺陷信息丢失问题，设计了MReLu和BReLu两种改进激活函数。在此基础上，为了增强神经网络模型的特征表达能力,提出了双层激活函数模型。最后，在公共数据集MNIST,CIFAR-10上进行了比较实验,网络主要参数有批处理大小(batch size)为32，最大迭代次数为1000次，学习率为0.0001，每经过5000次迭代衰减50%。实验结果表明，基于改进后的激活函数和应用双层激活函数所构造的卷积神经网络，大大减少了训练参数，不仅收敛速度更快，而且可以更加有效地提高分类的准确率。

Abstract:

The defect detection of road manhole cover is very important for road maintenance and safety. The paper proposes an improved convolutional neural network algorithm to achieve rapid and accurate detection of manhole cover defects. The algorithm improves the activation function model of convolutional neural network. For the Relu activation function, when the input is less than zero, the output is set to zero, which resluts in lossing most of the input information.Therefore,two improved activation functions, MReLu and BReLu, are designed. On this basis, in order to enhance the feature expression ability of neural network model, a two-layer activation function model is proposed. Finally, a large number of comparative experiments were performed on the proposed algorithm in the public data set MNIST, CIFAR-10,and The main parameters of the network are batch size of 32, the maximum number of iterations is 1000, the learning rate is 0.0001, and the attenuation is 50% after 5000 iterations.The experimental results show that the convolutional neural network based on the improved activation function and the application of the two-layer activation function greatly reduces the training parameters, not only the convergence speed is faster, but also can improve the classification accuracy more effectively.