计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211100100-5.doi: 10.11896/jsjkx.211100100
原慧琳1, 刘军涛2, 黄碧2, 韩真2, 冯宠2
YUAN Hui-lin1, LIU Jun-tao2, HUANG Bi2, HAN Zhen2, FENG Chong2
摘要: 随着工业的迅速发展,垃圾产生的数量呈爆炸式上升,使得垃圾处理成为一个世界性的难题。我国政府对环境的关切也逐渐加深,不断推出了各种垃圾分类政策以及法律法规以监督市民进行垃圾分类。垃圾处理特别是有害垃圾处理,如电子废弃物等,如果处理不当,会对环境产生恶劣影响。有害垃圾图像数据具有数据质量低、图像不清晰的特点,采集来自不同设备的图像样本又有明显差异,因此有害垃圾图像处理面临巨大挑战,同时有害废弃物分类结果关系到环境污染问题,且目前产出的垃圾数量巨大,要求具有较高的处理速度和准确度。文中提出了一种基于卷积神经网络和注意力机制的垃圾图像分类方法。该方法不需要对输入的图像进行手工提取特征,通过深度学习模型框架,弥补传统图像处理算法的不足,实现对有害垃圾准确、高效的分类,可以较好地识别多种类型的有害垃圾。经实验验证,所提方法在harmful-waste数据集上的准确率达到97.46%,相比其他算法模型,其模型训练时间更短,性能更优。利用深度学习的方法,部署自动化垃圾分类模型,对于环境保护有重要意义。
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