摘要
城市生活垃圾管理是影响环境和资源利用效率的主要环节,当前垃圾收运车主要依靠工人进行垃圾分类,这种方式效率不高,分类容易出错,而且工人工作环境也存在风险。利用图像识别技术自动识别垃圾类别,是提升垃圾处理智能化水平的关键方向,深度学习技术能够有效识别图片中的物体特征,在垃圾图像分类任务中展现出良好效果。因此,文章就如何设计基于深度学习的垃圾图像自动分类识别系统展开探讨,以期提高车载垃圾分类的效率和准确性。
Urban domestic waste management is the main link that affects the efficiency of environment and resource utilization,and the current garbage collection vehicle mainly relies on the workers to classify the garbage,which is inefficient,the classification is prone to errors,and there are risks in the working environment of the workers.The use of image recognition technology to automatically identify the category of garbage is a key direction to improve the level of garbage disposal intelligence,deep learning technology can effectively identify the characteristics of objects in the picture,and show good results in the garbage image classification task.Therefore,this paper discusses how to design a deep learning-based automatic garbage image classification and recognition system,in the hope of improving the efficiency and accuracy of on-board garbage classification.
作者
丁佳敏
郭小颖
王乐
李英
王璐
Ding Jiamin;Guo Xiaoying;Wang Le;Li Ying;Wang Lu(Shaanxi Fashion Engineering University,the School of Information Engineering,Xi'an 712046,China)
出处
《汽车电器》
2025年第8期86-88,共3页
Auto Electric Parts
基金
2025年度校级大学生创新创业训练计划项目(SXFUCIETP054)。
关键词
深度学习
车载垃圾
图像分类
deep learning
on-board garbage
image classification
作者简介
丁佳敏(2005-),女,研究方向为目标检测;郭小颖(2002-),女,研究方向为目标检测;王乐(1993-),女,硕士,助教,研究方向为人工智能;李英(1997-),女,硕士,助教,研究方向为图像处理、目标检测;王璐(1996-),女,硕士,助教,研究方向为数据分析与数据可视化。
