Abstract
Due to the lack of data available for training, deep learning hardly performed well in the field of garbage image classification. We choose the TrashNet data set which is widely used in the field of garbage image classification, and try to overcome data deficiencies in this field by optimizing the network structure. In this article, it is found that the deeper network and short-circuit connection, which are generally accepted in the field of deep learning, will not work well on the TrashNet data set. By analyzing and modifying the network structure, we propose an effective method to improve the network performance on TrashNet data set. This method widens the network by expanding branches, and then uses add layers to realize the fusion of feature information. It can make full use of feature information at slight additional computational cost. Using this method to replace the core structure of the Xception network, the performance of the improved network has been improved greatly. Finally, the M-b Xception network proposed by us achieves 94.34% classification accuracy on the TrashNet data set, and has certain advantages over some state-of-the-art methods on multiple indicators. The python code can be download from https://github.com/scp19801980/Trash-classify-M_b-Xception.
Highlights
With the development of human society, the problem of environmental pollution is becoming more and more serious [1], and environmental pollution has great harm to the earth and all its organisms [2]
It is found that the deeper network and short-circuit connection, which are generally accepted in the field of deep learning, will not work well on the TrashNet data set
This method widens the network by expanding branches, and uses add layers to realize the fusion of feature information
Summary
With the development of human society, the problem of environmental pollution is becoming more and more serious [1], and environmental pollution has great harm to the earth and all its organisms [2]. For the garbage image classification based on deep learning, people tend to use deeper neural network gradually. For the TrashNet, a small data set with a single background, the difficulty comes from the data set itself, i.e., the small amount of feature information, the small number of data samples, and the large similarity among classes In this case, classification performance is hardly to be improved by increasing network depth. ● A new method that can expand the branch of a specific network layer is proposed This method can widen network structure and extract feature information more effectively, which is beneficial for garbage image classification. We used this method to establish the M-b Xception that performed better on the TrashNet data set.
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