机电工程技术2024，Vol.53Issue(6)：205-208,4.DOI:10.3969/j.issn.1009-9492.2024.06.048
基于决策级融合的曳引钢带表面缺陷检测方法
Surface Defect Detection Method for Traction Steel Strip Based on Decision-level Fusion
雷高阳 1王凯旋 2李俊杰 3李根生 4李海超5
作者信息
- 1. 中国矿业大学信息与控制工程学院,江苏徐州 221000
- 2. 徐州工业职业技术学院信息工程学院,江苏徐州 221140
- 3. 河南大有能源股份有限公司新安煤矿,河南洛阳 471842
- 4. 河南科技大学车辆与交通工程学院,河南洛阳 471000
- 5. 杭州奥立达电梯有限公司,杭州 311600
- 折叠
摘要
Abstract
Due to the harsh working environment of the elevator traction steel strip,defects are prone to appear on the surface of the steel strip.In addition,the complex and dim environment makes it difficult to identify and locate the surface defect.Therefore,a defect detection method for traction steel strips is proposed based on decision level fusion.The pre-trained models of YOLOv4 and SSD are fine-tuned using transfer learning,which are applied to defect detection of traction steel strips to obtain different original detection results.The proposed method is used to fuse the original detection results of YOLOv4 and SSD models at the decision level,thereby improving the accuracy of surface defect recognition and localization of steel strips.The effectiveness and reliability of the proposed method are tested using the public dataset of surface defects in steel strips from Northeastern University.The defect identification and localization results show that the proposed method can fully utilize the original detection results of YOLOv4 and SSD models.By fusing the original detection results at the decision level,the high accuracy is achieved for defect identification and localization,where the accuracy and recall increase of about 15%,and the handover to merger ratio of about 0.6.It is of great significance for defect detection and maintenance of elevator traction steel strips,and further ensures the safe and reliable operation of the elevator.关键词
曳引钢带/表面缺陷/缺陷检测/决策级融合/图像识别Key words
traction steel strip/surface defects/defect detection/decision-level fusion/image recognition分类
信息技术与安全科学引用本文复制引用
雷高阳,王凯旋,李俊杰,李根生,李海超..基于决策级融合的曳引钢带表面缺陷检测方法[J].机电工程技术,2024,53(6):205-208,4.
