基于深度学习的合金钢丝绳缺陷智能检测识别方法

By using deep learning and standard torsion analysis, defects can be directly identified from the cross-sectional images of alloy steel wire ropes, solving the problems of unintuitive detection and performance impact caused by the need for preprocessing in existing technologies, and achieving efficient and accurate defect detection.

CN121347598BActive Publication Date: 2026-07-17SUZHOU NEW BEST WIRE TECH CO LTD
View PDF 3 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SUZHOU NEW BEST WIRE TECH CO LTD
Filing Date
2025-10-23
Publication Date
2026-07-17

Smart Images

  • Figure CN121347598B_ABST
    Figure CN121347598B_ABST
Patent Text Reader

Abstract

本发明公开了基于深度学习的合金钢丝绳缺陷智能检测识别方法，涉及钢丝绳检测技术领域，包括：使用标准扭转分析法获取标准边缘参数以及标准核心参数，将标准扭转分析法录入深度神经网络内，得到可识别网络，使用可识别网络对合金钢丝绳的缺陷进行检测；本发明用于解决现有的合金钢丝绳缺陷智能检测识别方法中，检测时需先对钢丝绳进行处理，导致无法直观高效地对钢丝绳进行缺陷检测，且对钢丝绳进行处理后会存在影响钢丝绳自身性能的隐患，同时对于已扭转的钢丝绳会存在增加检测难度，降低检测效率的问题。
Need to check novelty before this filing date? Find Prior Art