Li Zijian, Yao Yong, Wen Runyuan, Liu Qiyang
School of Computer Science and Technology, Xidian University, Xi'an 710126, China.
Sensors (Basel). 2024 Oct 18;24(20):6717. doi: 10.3390/s24206717.
Defect detection in transparent materials typically relies on specific lighting conditions. However, through our work on defect detection for aircraft glass canopies, we found that using a single lighting condition often led to missed or false detections. This limitation arises from the optical properties of transparent materials, where certain defects only become sufficiently visible under specific lighting angles. To address this issue, we developed a dual-modal illumination system that integrates both forward and backward lighting to capture defect images. Additionally, we introduced the first dual-modal dataset for defect detection in aircraft glass canopies. Furthermore, we proposed an attention-based dual-branch modal fusion network (ADMF-Net) to enhance the detection process. Experimental results show that our system and model significantly improve the detection performance, with the dual-modal approach increasing the mAP by 5.6% over the single-modal baseline, achieving a mAP of 98.4%. Our research also provides valuable insights for defect detection in other transparent materials.
透明材料中的缺陷检测通常依赖于特定的照明条件。然而，通过我们在飞机玻璃座舱盖缺陷检测方面的工作，我们发现使用单一照明条件常常会导致漏检或误检。这种局限性源于透明材料的光学特性，某些缺陷只有在特定照明角度下才会变得足够明显。为了解决这个问题，我们开发了一种双模态照明系统，该系统集成了前向照明和后向照明以捕获缺陷图像。此外，我们引入了首个用于飞机玻璃座舱盖缺陷检测的双模态数据集。此外，我们提出了一种基于注意力的双分支模态融合网络（ADMF-Net）来增强检测过程。实验结果表明，我们的系统和模型显著提高了检测性能，双模态方法比单模态基线的平均精度均值（mAP）提高了5.6%，达到了98.4%的mAP。我们的研究还为其他透明材料的缺陷检测提供了有价值的见解。
