Light spot detection of diamond wire based on deep learning

FENG Zongqiang; YING Yipeng; ZHANG Fujun; YU Yongbo; LIU Yi

doi:10.37188/OPE.20233115.2260

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Light spot detection of diamond wire based on deep learning

Information Sciences | 更新时间：2026-09-11

- Light spot detection of diamond wire based on deep learning
- Optics and Precision Engineering Vol. 31, Issue 15, Pages: 2260-2272(2023)
- 作者机构：
  
  燕山大学机械工程学院，河北秦皇岛 066004
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233115.2260
  CLC： TP391.4
- Received：27 August 2022，
  
  Revised：2022-10-19，
  
  Published：10 August 2023
- 稿件说明：
移动端阅览
丰宗强,应一鹏,章甫君等.基于深度学习的金刚线光斑点检测[J].光学精密工程,2023,31(15):2260-2272.

FENG Zongqiang,YING Yipeng,ZHANG Fujun,et al.Light spot detection of diamond wire based on deep learning[J].Optics and Precision Engineering,2023,31(15):2260-2272.
丰宗强,应一鹏,章甫君等.基于深度学习的金刚线光斑点检测[J].光学精密工程,2023,31(15):2260-2272. DOI： 10.37188/OPE.20233115.2260.

FENG Zongqiang,YING Yipeng,ZHANG Fujun,et al.Light spot detection of diamond wire based on deep learning[J].Optics and Precision Engineering,2023,31(15):2260-2272. DOI： 10.37188/OPE.20233115.2260.

摘要

金刚线断线检测是金刚线生产过程中的重要环节。针对现有接触式检测敏感度低、断线反馈滞后等问题，提出了一种基于机器视觉检测强光下金刚线反射的光斑点的非接触式断线检测方法。在金刚线光斑点检测的嵌入式平台上，针对传统图像处理的光斑点检测操作复杂、易受外部光照影响的局限性，研究了基于深度学习的光斑点目标检测，对多种Yolo系列模型进行了训练部署，针对原有模型网络层次较深、模型体积较大，在嵌入式设备中存在检测实时性较差的问题，提出了一种基于Yolox改进的轻量化目标光斑点检测模型MCA-Yolox，利用MobileNetV3轻量化特征提取网络替换Yolox模型的主干特征提取网络，对模型进行轻量化改进，然后利用深度可分离卷积和倒残差结构对加强特征提取网络进行了轻量化改进。结合CA注意力机制提高了轻量化模型的检测精度。最后，将改进后的模型部署于嵌入式平台。实验结果表明，改进后模型MCA-Yolox的大小和运算量减小到Yolox模型的1/3以下，与同样规模的Yolox-Tiny和Yolov4-Tiny相比具有更高的检测精度，模型的mAP提升了1%以上，加速优化后检测速度可达30 frame/s，提供了一种基于深度学习检测金刚线断线的完整工业检测方案。

Abstract

Break detection is an important part of the diamond wire production process. To address the problems of low sensitivity and lag feedback of existing contact detection， a non-contact wire break detection method is proposed based on machine vision detection of light spots reflected by diamond lines under strong light. Here， the study addresses the limitations of complex spot detection operation and ease of influence from external illumination of traditional image processing by investigating spot target detection on the embedded platform of diamond line spot detection using deep learning. A variety of Yolo-type models were trained and deployed. The problem of poor real-time detection in embedded devices due to the deep network level and large model volume of the original model was also addressed through a lightweight target spot detection model MCA-Yolox based on Yolox. The MobileNetV3 lightweight feature extraction network was used to replace the backbone feature extraction network of the Yolox model， and the model was lightweighted. Then， the enhanced feature extraction network was lightweight using the deep separable convolution and inverted residual structures. Then， combined with the CA attention mechanism， the detection accuracy of the lightweight model was improved. Finally， the improved model was deployed on the embedded platform. The experimental results show that the size and computation amount of the improved model， MCA-Yolox， are reduced to less than 1/3 those of the Yolox model， and compared with Yolox-Tiny and Yolov4-Tiny of the same scale， it has higher detection accuracy. The mAP of the model increased by more than 1%， and the detection speed can reach 30 frames/s after accelerated optimization. In summary， this paper presents a complete industrial detection scheme based on deep learning to detect diamond wire breaks.

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references

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