Garbage classification algorithm based on improved lightweight network shufflenetV2

Tianhui Li, Xi Li, Congcong Li
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Abstract

Aiming at the problem that the current deep learning garbage classification model cannot take into account high accuracy, small model size and high real-time performance, a garbage classification algorithm based on the improved lightweight network Shufflenet V2 is proposed. By improving the basic unit of Shufflenet V2 and introducing the attention mechanism module of CBAM, the feature extraction ability of the network was enhanced. Reduce the number of units used in each stage layer in the network structure, reduce the amount of parameters and calculation of the model, and at the same time, avoid excessively deep networks to extract irrelevant information, resulting in loss of accuracy. Replace the ReLU activation function with the Leakyrelu activation function to increase the richness of the extracted feature information. Use label smoothing loss function to reduce the negative impact due to sample class imbalance. The experimental results show that the accuracy of the algorithm on the self-built dataset is 81.26%. The parameter quantity of the model is about 0.917M, and the calculation quantity is about 92.75MFlops and 182. 32M MAdd. The accuracy of the algorithm is better than Resnet101, and the parameter quantity is only 1/44 of Resnet101, which provides a reference for the deployment and application of garbage classification and identification method in resource constrained devices such as mobile terminals.
基于改进的轻量级网络shufflenetV2的垃圾分类算法
针对当前深度学习垃圾分类模型不能兼顾高精度、小模型尺寸和高实时性的问题,提出了一种基于改进型轻量级网络Shufflenet V2的垃圾分类算法。通过改进Shufflenet V2的基本单元,引入CBAM的注意机制模块,增强了网络的特征提取能力。减少网络结构中各阶段层使用的单元数量,减少模型的参数和计算量,同时避免网络过于深度提取不相关信息,导致准确性损失。将ReLU激活函数替换为Leakyrelu激活函数,增加提取特征信息的丰富度。使用标签平滑损失函数减少样本类不平衡的负面影响。实验结果表明,该算法在自建数据集上的准确率为81.26%。模型的参数量约为0.917M,计算量约为92.75MFlops和182。MAdd 32米。该算法的准确率优于Resnet101,参数数量仅为Resnet101的1/44,为垃圾分类识别方法在移动终端等资源受限设备中的部署和应用提供了参考。
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