基于ResNet网络的垃圾分类模型研究

Research on Garbage Classification Model Based on ResNet Network

摘要: 垃圾分类问题对于环境保护和可持续发展至关重要。传统的垃圾分类模型在处理复杂场景时存在性能瓶颈，而ResNet网络以其深度残差学习结构在图像识别任务中取得了显著的成功。为进一步提升垃圾分类模型的性能，本文在ResNet网络基础上进行了改进，引入了注意力机制和增强的特征提取策略。通过对垃圾分类数据集的训练，验证了改进ResNet网络在垃圾分类准确性和泛化性能上的优势，所提出的模型在不同垃圾类别上均取得了较高的分类准确度，实验结果表明相较于ResNet50，本文提出的垃圾分类模型提高了1.9%的准确率。本研究为垃圾分类问题的解决提供了一种更有效的解决方案，也为其他复杂图像分类问题提供了有益的借鉴。

Abstract: The problem of garbage classification is crucial for environmental protection and sustainable development. Traditional garbage classification models face performance bottlenecks when dealing with complex scenes, while the ResNet network has achieved significant success in image recognition tasks due to its deep residual learning structure. In order to further enhance the performance of garbage classification models, this paper improved upon the ResNet network by introducing attention mechanisms and enhanced feature extraction strategies. Through training on a garbage classification dataset, the improved ResNet network’s advantages in accuracy and generalization performance for garbage classification were verified. The proposed model achieved higher classification accuracy for different garbage categories, demonstrating a 1.9% improvement in accuracy compared to ResNet50. This research provides a more effective solution for the problem of garbage classification and offers beneficial insights for other complex image classification problems.