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材料导报  2025, Vol. 39 Issue (20): 24110020-8    https://doi.org/10.11896/cldb.24110020
  金属与金属基复合材料 |
基于红外光图像的报废汽车非磁金属回收利用技术研究
贾有东1,4, 刘剑雄1,*, 李欣治4, 姚思博2,3, 李正芳4, 曾家兴1
1 昆明理工大学机电工程学院,昆明 650500
2 东北大学机械工程与自动化学院,沈阳 110819
3 航空动力装备振动及控制教育部重点实验室,沈阳 110819
4 昆明学院机电工程学院,昆明 650214
Research on Non-magnetic Metal Recycling Technology of End-of-life Vehicles Based on Infrared Image
JIA Youdong1,4, LIU Jianxiong1,*, LI Xinzhi4, YAO Sibo2,3, LI Zhengfang4, ZENG Jiaxing1
1 School of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
2 School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
3 Key Laboratory of Aviation Power Equipment Vibration and Control, Ministry of Education, Shenyang 110819, China
4 School of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China
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摘要 传统的物理分选技术,如磁选、涡电流分选和密度分离,难以有效地将报废汽车中非磁金属从混合物料中分离出来。本工作基于红外光图像提出了一种高效的报废汽车非磁金属分选方法,并通过响应曲面法,以报废汽车铜、铝及不锈钢破碎物热成像图像为研究对象,系统研究了不同材料在不同温度和发射率条件下对识别精度的影响。通过实验,确定了铜、铝及不锈钢混合物热成像图像采集的最佳工艺参数组合为:加热台温度设定为326.89 ℃、热成像仪设置发射率为0.89时,预测的mAP50值为0.994 2,mAP50-95值为0.904 0。本工作为在混合物中识别出铜、铝和不锈钢提供了有效的技术手段,从而提升材料回收的效率和经济价值。
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贾有东
刘剑雄
李欣治
姚思博
李正芳
曾家兴
关键词:  报废汽车  非磁金属回收  红外热成像  YOLO算法  分类识别    
Abstract: Traditional physical separation technologies, such as magnetic separation, eddy current separation, and density separation, struggle to effectively separate non-magnetic metals from mixed materials in end-of-life vehicles. Thiswork proposed an efficient non-magnetic metal separation method for end-of-life vehicles based on infrared images. Focused on the thermal imaging images of copper, aluminum, and stainless steel fragments from end-of-life vehicles, systematically discussed the impact of different materials on recognition accuracy under various temperature and emissivity conditions using the response surface method. Through experiments, the optimal process parameters for acquiring thermal imaging images of copper, aluminum, and stainless steel mixtures were determined as fellows: the heating table temperature is 326.89 ℃, the emissivity of the thermal imager is 0.89, the predicted mAP50 and mAP50-95 values are 0.994 2 and 0.904 0, respectively. This findings provide an effective technical approach for identifying copper, aluminum, and stainless steel in mixtures, thereby enhancing material recovery efficiency and economic value.
Key words:  end-of-life vehicles    non-magnetic metal recovery    infrared thermal imaging    YOLO algorithm    classification recognition
发布日期:  2025-10-27
ZTFLH:  TP319  
基金资助: 国家自然科学基金(52065034;51505119);中央高校基本科研业务费专项资金资助(N2303002)
通讯作者:  *刘剑雄,博士,昆明理工大学机电工程学院教授,博士研究生导师。目前主要从事材料断裂机理及强度理论、现代装备研究开发等方面的研究工作。jxlkmust@163.com   
作者简介:  贾有东,昆明理工大学博士研究生,昆明学院教师。主要从事机器视觉技术和人工智能算法在金属材料分选领域的应用研究。
引用本文:    
贾有东, 刘剑雄, 李欣治, 姚思博, 李正芳, 曾家兴. 基于红外光图像的报废汽车非磁金属回收利用技术研究[J]. 材料导报, 2025, 39(20): 24110020-8.
JIA Youdong, LIU Jianxiong, LI Xinzhi, YAO Sibo, LI Zhengfang, ZENG Jiaxing. Research on Non-magnetic Metal Recycling Technology of End-of-life Vehicles Based on Infrared Image. Materials Reports, 2025, 39(20): 24110020-8.
链接本文:  
https://www.mater-rep.com/CN/10.11896/cldb.24110020  或          https://www.mater-rep.com/CN/Y2025/V39/I20/24110020
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