基于残差注意力机制的垃圾图像分类

GARBAGE IMAGE CLASSIFICATION BASED ON RESIDUAL ATTENTION MECHANISM

  • 摘要: 针对垃圾图像自动识别的问题,提出一种基于残差注意力机制与双损失函数的分类算法。该方法在采用预训练模型的基础上,通过构建残差注意力模块,使模型在聚焦于局部特征信息的同时关注全局特征信息;引入标签平滑与Focal Loss相结合的双损失函数,有效缓解类别不均衡引起的过拟合问题。实验结果表明,该方法有效提升了模型的特征提取能力与分类精度,在TrashNet、华为数据集和扩展数据集上的分类准确率分别为98.32%、96.92%和96.66%,优于当前表现最好的方法。

     

    Abstract: To solve the problem of garbage image automatic recognition, a classification algorithm based on residual attention mechanism and dual loss function is proposed. The method was based on the pre-training model. The model focused on local feature information and global feature information at the same time by constructing residual attention module. A dual loss function combining label smoothing and Focal Loss was added, which effectively alleviated the problem of over-fitting caused by category imbalance. The experimental results show that the proposed method can effectively improve the feature extraction capability and classification accuracy of the model. The classification accuracy on the TrashNet, Huawei datasets and extended datasets is 98.32%, 96.92% and 96.66%, respectively, which is better than the current best performance method.

     

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