基于深度学习的垃圾图片处理与识别

针对城市环境卫生提出的对市民生活垃圾进行分类回收的要求,考虑计算机卷积神经网络在图片分类中的强大表现,提出了基于深度学习中卷积神经网络对垃圾图片处理以及输出识别的新模型与方法。针对目前图像局部特征表达存在的复杂性,模糊性等不足,采用特征多层池化以及系统神经网络学习的方式进行优化。同时在ResNet101模型的基础上设计并构建了基于CNN(Convolutional Neural Network)算法的新模型框架,此系统模型也能实现端与端的实时识别。新模型提高了对训练样本图像信息提取的精确度以及图片识别的准确率,实验表明识别准确率平均提高了10%。为未来实现人工智能垃圾分类提供图像识别模型基础。

Deep Learning Based Garbage Image Processing and Recognition

In response to the requirement of urban environmental health to classify and recycle citizens'household garbage,a new model and method for garbage image processing and output recognition based on convolutional neural networks in deep learning is proposed considering the powerful performance of computer convolutional neural networks in image classification.For the short?comings of the current image local feature representation such as complexity and ambiguity,feature multilayer pooling and system?atic neural network learning are used for optimization.A new model based on the CNN(Convolutional Neural Network)algorithm is also designed and built on the basis of the ResNet101 model,and this system model can also achieve end-to-end real-time recog?nition.The new model improves the accuracy of image information extraction from training samples and the accuracy of image rec?ognition,and experiments show that the recognition accuracy is improved by 10%on average.It provides the basis of image recog?nition model for future implementation of artificial intelligence garbage classification.