基于多特征融合的强光元件在线损伤检测

杨鹏; 李乾; 赵强云; 罗鑫宇; 雷陈栋; 刘冠军

doi:10.12382/bgxb.2025.0169

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基于多特征融合的强光元件在线损伤检测

更新时间：2026-05-06

- 基于多特征融合的强光元件在线损伤检测
- Online Damage Detection of High-power Optical Elements Based on Multi-feature Fusion
- 兵工学报 2026年47卷第4期页码：250169
- 作者机构：
  
  国防科技大学智能科学学院，湖南长沙 410073
  国防科技大学装备状态感知与敏捷保障全国重点实验室，湖南长沙 410073
  国防科技大学前沿交叉学科学院，湖南长沙 410073
  国防科技大学南湖之光实验室，湖南长沙 410073
  92292 部队，山东青岛 266000
- 作者简介：
  
  通信作者邮箱：nudtyp7894@163.com
- 基金信息：
- DOI：10.12382/bgxb.2025.0169
  中图分类号： TN214;TJ95;TP751
- 收稿：2025-03-11，
  
  网络首发：2026-03-02，
  
  纸质出版：2026-04
- 稿件说明：
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杨鹏, 李乾, 赵强云, 等. 基于多特征融合的强光元件在线损伤检测[J]. 兵工学报, 2026,47(4):250169.

YANG Peng, LI Qian, ZHAO Qiangyun, et al. Online Damage Detection of High-power Optical Elements Based on Multi-feature Fusion[J]. Acta Armamentarii, 2026, 47(4): 250169.
杨鹏, 李乾, 赵强云, 等. 基于多特征融合的强光元件在线损伤检测[J]. 兵工学报, 2026,47(4):250169. DOI： 10.12382/bgxb.2025.0169.

YANG Peng, LI Qian, ZHAO Qiangyun, et al. Online Damage Detection of High-power Optical Elements Based on Multi-feature Fusion[J]. Acta Armamentarii, 2026, 47(4): 250169. DOI： 10.12382/bgxb.2025.0169.

摘要

针对强光元件在线损伤检测对实时性、高检测率与低虚警率的复合要求，提出一种融合光热辐射与可见光成像的在线检测方案，基于红外与可见光双路图像同步定义了高温与亮斑区域特征，结合反相去雾增强与Canny边缘检测实现特征高效提取，通过构建双阈值告警机制（预警80℃、停机200℃）、位置偏差匹配策略及多阈值融合决策模型降低误判率。实验结果表明：系统平均特征提取耗时17ms，决策耗时137ms，检测准确率达97.2%，虚警率为1.6%，可在致命故障前2.52s发出预警、1.64s前触发停机告警，为人工干预预留充分时间，显著提升了检测的可靠性与工程实用性。

Abstract

Addressing the complex requirements of real-time performance

high detection rate

and low false alarm rate for online damage detection of high-power optical elements

an online detection scheme that integrates photothermal radiation and visible light imaging is proposed. The characteristics of hightemperature and bright spot region are defined based on the synchronization of infrared and visible light dual-channel images

and the efficient feature extraction is achieved by combining inverse fog enhancement and Canny edge detection. A dual-threshold alarm mechanism (warning at 80℃

shutdown at 200℃)

a position deviation matching strategy

and a multi-threshold fusion decision model are constructed to reduce the false alarm rate. Experimental results show that the average feature extraction time and decision-making time of the developed system are 17 ms and 137 ms

respectively

with a detection accuracy of 97.2% and a false alarm rate of 1.6%.It can issue a warning 2.52s and trigger a shutdown alarm 1.64s before a fatal failure

allowing sufficient time for manual intervention. This significantly improves the reliability and engineering practicality of the detection.

关键词

Keywords

references

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