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作 者:吴昆鹏 石杰[1] 杨朝霖 邓能辉 WU Kunpeng;SHI Jie;YANG Chaolin;DENG Nenghui(National Engineering Technology Research Center of Flat Rolling Equipment,University of Science and Technology Beijing,Beijing 100083,China;Design and Research Institute Co.,Ltd.,University of Science and Technology Beijing,Beijing 100083,China)
机构地区:[1]北京科技大学国家板带生产先进装备工程技术研究中心,北京100083 [2]北京科技大学设计研究院有限公司,北京100083
出 处:《组合机床与自动化加工技术》2023年第7期157-161,共5页Modular Machine Tool & Automatic Manufacturing Technique
基 金:国家自然科学基金项目(B1822573)。
摘 要:冷床区范围较大、结构紧凑、安装金属探测仪困难且成本较高,常用的自动化二级跟踪策略无法有效地完成对该场景下钢板的跟踪。通过利用改进的SOLO实例分割算法构建冷床区钢板检测的视觉模型,识别得到冷床区各个钢板对象的坐标位置和长宽尺寸;再结合逻辑跟踪模型形成多模型匹配跟踪策略,能够识别到行车下料、冷床卡钢等特殊情况,实现自动下料跟踪和异常预警。结果表明,改进的SOLO实例分割方法在6种不同的环境场景下平均mAP(mean average precision)提升到98.14%,平均mIoU(mean intersection over union)提升到98.72%,并且实现了29.1 fps的处理速率,满足了生产条件中对于准确率和实时性的要求。The cooling bed area is large in scope,compact in structure,difficult to install metal detectors and high in cost.The commonly used automatic secondary tracking strategy cannot effectively track the steel plates in this scenario.The visual model of steel plate detection in the cooling bed area is constructed by using the improved SOLO instance segmentation algorithm,and the coordinate position,length and width of each steel plate object in the cooling bed area are identified;Combined with the logic tracking model,a multi model matching tracking strategy is formed,which can identify the special situations such as crane blanking and steel clamping in the cooling bed,and realize automatic blanking tracking and abnormal early warning.The experimental results show that the improved SOLO instance segmentation method can increase the average mAP(mean average precision)to 98.14%,and the average mIoU(mean intersection over union)to 98.72%in six different environmental scenarios,and achieve a processing rate of 29.1 fps,meeting the requirements for accuracy and real-time in production conditions.
关 键 词:冷床 自动下料 实例分割 计数模块 逻辑跟踪 匹配跟踪策略
分 类 号:TH161[机械工程—机械制造及自动化] TG502[金属学及工艺—金属切削加工及机床]
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