基于轻量级残差网路的垃圾图片分类方法

近年来,我国生活垃圾总量每年以10％的速度增长.但是生活垃圾的分类处理能力及技术相对比较有限和落后.基于机器视觉的分类方法一直是广泛使用的方法,而传统的视觉分类网络目前面临着参数多、计算量大、分类精度不高和分类时间长的问题.因此,提出使用最大平均组合池化(Max-AVE Pooling)代替ResNet-50Bottl...

GARBAGE IMAGE CLASSIFICATION BY LIGHTWEIGHT RESIDUAL NETWORK

In recent years, the total domestic garbage in China has been increasing at a rate of 10% per year. However, the technology for the classification and treatment of domestic garbage is relatively limited and backward. The classification method based on machine vision has been widely used. Traditional visual classification net currently faces the problems of sophisticated parameters, large amount of calculation, low classification accuracy, and long classification time. Therefore, this paper proposed to use Max-AVE Pooling instead of Max Pooling or AVE Pooling in ResNet-50Bottleneck, and use the depth separable convolution instead of the standard convolution method in ResNet-50Bottleneck to classify junk images. The experimental results showed that the lightweight residual network (MaxAVE-Pooling-MobileNet-18, MAPMobileNet-18) proposed in this paper could significantly reduce the parameter amount by 10 times and the calculation amount by 14 times, and slightly improve the accuracy compared with the classical classification network. It is very suitable for the implementation and application of mobile phones and embedded devices.