YOLO-DSM method for detecting multi-background visual micro-defects in TFT-LCD panels

KONG Xiangfei; WANG Sen; ZHAO Lin; CHEN Mingfang

doi:10.13471/j.cnki.acta.snus.ZR20240261

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YOLO-DSM method for detecting multi-background visual micro-defects in TFT-LCD panels

Research articles | 更新时间：2025-11-14

- YOLO-DSM method for detecting multi-background visual micro-defects in TFT-LCD panels
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 64, Issue 2, Pages: 129-137(2025)
- 作者机构：
  
  1.昆明理工大学机电工程学院，云南昆明 650500
  2.河南中烟工业有限责任公司，河南郑州 450000
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.ZR20240261
  CLC： TP394.1;TH691.9
- Received：20 August 2024，
  
  Accepted：05 September 2024，
  
  Published Online：14 October 2024，
  
  Published：15 March 2025
- 稿件说明：
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孔祥飞,王森,赵林等.可在TFT-LCD面板中实现多背景视觉细微缺陷检测的YOLO-DSM方法[J].中山大学学报(自然科学版)(中英文),2025,64(02):129-137.

KONG Xiangfei,WANG Sen,ZHAO Lin,et al.YOLO-DSM method for detecting multi-background visual micro-defects in TFT-LCD panels[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2025,64(02):129-137.
孔祥飞,王森,赵林等.可在TFT-LCD面板中实现多背景视觉细微缺陷检测的YOLO-DSM方法[J].中山大学学报(自然科学版)(中英文),2025,64(02):129-137. DOI： 10.13471/j.cnki.acta.snus.ZR20240261.

KONG Xiangfei,WANG Sen,ZHAO Lin,et al.YOLO-DSM method for detecting multi-background visual micro-defects in TFT-LCD panels[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2025,64(02):129-137. DOI： 10.13471/j.cnki.acta.snus.ZR20240261.

摘要

提出了一种基于YOLO-DSM深度学习图像检测模型。首先，在每个Dark模块后引入HMU模块，以提高TFT-LCD面板上目标缺陷的检测精度。将原始SPP替换为SSMA，使得网络更加关注背景低对比度目标。其次，引入DSM模块以帮助网络增强有用特征且抑制无用特征，增强语义信息的集成。最后，用ODConv模块替换原始网络的下采样卷积，细化局部特征映射，实现局部缺陷特征的充分提取。在自制的TFT-LCD缺陷数据集中，与当前较为先进的算法进行对比。结果表明，YOLO-DSM网络在mAP精度方面达到了97.40%，且FPS达到了77.42帧，可满足TFT-LCD缺陷任务检测要求。

Abstract

A deep learning image detection model based on You Only Look Once-Double Spatial-Squeeze Module （YOLO-DSM） is proposed. First，the Hierarchical Mixed-scale Unit （HMU） module is introduced after each Dark module to improve the detection accuracy of target defects on TFT-LCD panels. The original Spatial Pyramid Pooling（SPP） is replaced with Simple Spatial Mlp Attention（SSMA） to enable the network to focus more on targets with low contrast against the background. Second， the Double Spatial-Squeeze Module（DSM） is introduced to help the network enhance useful features and suppress useless ones， thereby enhancing the integration of semantic information. Finally， the Omni-dimensional Dynamic Convolution（ODConv） module replaces the down-sampling convolution of the original network to refine local feature mapping and achieve full extraction of local defect features. In comparative experiments on a self-made TFT-LCD defect dataset， the YOLO-DSM network achieved an mAP accuracy of 97.40% and an FPS of 77.42 frames. This meets the requirements of TFT-LCD defect detection tasks.

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references

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