基于卷积神经网络的垃圾图像分类算法

垃圾分类作为资源回收利用的重要环节之一, 可以有效地提高资源回收利用效率, 进一步减轻环境污染带来的危害. 随着现代工业逐步智能化, 传统的图像分类算法已经不能满足垃圾分拣设备的要求. 本文提出一种基于卷积神经网络的垃圾图像分类模型(Garbage Classification Network, GCNet). 通过构建注意力机制, 模型完成局部和全局的特征提取, 能够获取到更加完善、有效的特征信息; 同时, 通过特征融合机制, 将不同层级、尺寸的特征进行融合, 更加有效地利用特征, 避免梯度消失现象. 实验结果证明, GCNet在相关垃圾分类数据集上取得了优异的结果, 能够有效地提高垃圾识别精度.

Classification Algorithm of Garbage Images Based on Convolutional Neural Network

Garbage classification, as one of the important links of resource recycling, can effectively improve the efficiency of resource recycling and further reduce the harm caused by environmental pollution. With the development of modern industry, traditional image classification algorithm cannot meet the requirements of garbage sorting equipment. This study proposes a garbage classification model based on convolutional neural networks (Garbage Classification Network, GCNet). By constructing the attention mechanism, the model completes extracting the local and global features and can obtain perfect and effective feature information. At the same time, the feature fusion mechanism is used to fuse features at different levels and sizes, which can effectively use features and prevent gradient from vanishing. The experimental results prove that GCNet has achieved excellent results on garbage classification datasets and can effectively improve the accuracy of garbage classification.