基于ResNet算法的垃圾图像识别分类研究

Research on garbage image recognition and classification based on RESNET algorithm

伴随着社会发展和生活质量稳步提高,垃圾如何处理问题显得尤为重要。该研究采用深度神经网络算法对实际生活场景中的40种垃圾图片进行识别分类,通过优化ResNet算法提升识别精度,识别率为99.4%。为合理解决垃圾分类的难题,有效提升资源利用率,减少环境污染提供一定的理论依据。

with the development of society,the quality of people’s life has been steadily improved,and the problem of garbage disposal has become increasingly apparent.In this study,the deep neural network algorithm is used to recognize and classify 40 kinds of garbage images in real life scenes.The recognition accuracy is improved by optimizing RESNET algorithm,and the recognition rate is 99.4%.In order to solve the problem of waste classification,improve the utilization rate of resources and reduce environmental pollution,a certain theoretical basis is provided.