基于改进残差网络的扬尘图像识别方法

当前利用深度学习方法进行扬尘图像识别的研究较少,一些传统的方法使得扬尘图像的识别率较低.针对这种情况,提出一种基于改进残差网络的扬尘识别方法.该方法将ResNet-50网络应用到扬尘数据集中,并对其网络结构进行了改进.加入空间金字塔池化以解决输入图像尺寸不固定的问题,并且将金字塔池的策略改为平均池化,将扩大特征图的方法应用到主干网络中,有利于提取到更加细粒度的特征,提升模型的性能,从而提高识别率.实验结果表明,该方法具有很高的精确度,为扬尘识别提供了一种有效的方案.

Dust Image Recognition Method Based on Improved Residual Network

At present, there are few studies on dust image recognition using the deep learning method, and the recognition rate of dust images is low due to the application of some traditional methods. In view of this situation, a dust identification method based on an improved residual network is proposed. The method applies ResNet-50 network to a dust data set, and the network structure is improved. Then, spatial pyramid pooling is added to solve the problem that the size of the input images is not fixed. In addition, the pyramid pooling is changed to average pooling, and the method of expanding a feature graph is applied to the backbone network, which is conducive to extract more fine-grained features, improve the performance of the model, and increase the recognition rate. In conclusion, the proposed method has high accuracy and provides an effective scheme for dust identification.