| 刘亚蒙,赵友全,孙振涛,陈宸.构建改进RT-DETR算法检测隐形眼镜环状波纹缺陷[J].电子测量与仪器学报,2024,38(5):1-9 |
| 构建改进RT-DETR算法检测隐形眼镜环状波纹缺陷 |
| Constructing an enhanced RT-DETR algorithm for detectingannular ripple defects in contact lenses |
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| DOI： |
| 中文关键词: 隐形眼镜 环状波纹 表面缺陷检测 RT-DETR |
| 英文关键词:contact lenses circular ripples surface defect detection RT-DET |
| 基金项目:中文基金项目国家自然科学基金（42177439）项目资助 |
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| Author | Institution |
| Liu Yameng | School of Precision Instrument and OptoElectronics Engineering, Tianjin University, Tianjin 300072, China |
| Zhao Youquan | School of Precision Instrument and OptoElectronics Engineering, Tianjin University, Tianjin 300072, China |
| Sun Zhentao | School of Precision Instrument and OptoElectronics Engineering, Tianjin University, Tianjin 300072, China |
| Chen Chen | School of Precision Instrument and OptoElectronics Engineering, Tianjin University, Tianjin 300072, China |
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| 中文摘要: |
| 环状波纹是隐形眼镜制造过程中水凝胶材料分布不均匀造成的、在镜体边沿向内环形收缩的表面缺陷，因在投影检测中难以发现而造成产品质量不良，环状波纹缺陷检测是隐形眼镜产品制造的技术难题之一。本文根据该缺陷特征，搭建了圆环光源照明成像系统，采集了环状波纹缺陷图像模型数据库，引入了一种基于改进RT-DETR的隐形眼镜环状波纹缺陷轻量级检测算法。首先，将RT-DETR原始ResNet18主干提取网络中的BasicBlock替换为轻量级FasterNetBlock。然后，在RT DETR的Neck部分加入SimAM三通道注意力机制，提高模型的准确度。最后，将GIoU替换为MPDIoU损失函数加快收敛速度，提高检测精度。实验结果表明，相比于原始的RT-DETR算法，改进后的RT-DETR算法在隐形眼镜环状波纹数据库上的mAP@0.5达到了94%，提高了3.1%，Params和FLOPs相比于原始的算法分别降低了15.6%和13%。该算法极大地减小了计算量，有效提高了隐形眼镜环状波纹缺陷的均值平均精度，有望突破隐形眼镜环状波纹缺陷在线检测的技术难题。 |
| 英文摘要: |
| Circular ripples are surface defects in the manufacturing process of contact lenses, resulting from uneven distribution of hydrogel materials, causing a concentric contraction along the edge of the lens. These defects are challenging to detect in projection inspections, leading to poor product quality. Detecting circular ripple defects poses a technical challenge in the production of contact lenses. In this study, a circular ring illumination imaging system was constructed based on the characteristics of this defect. An image model database of circular ripple defects was collected, and a lightweight detection algorithm for contact lens circular ripple defects based on an improved RT-DETR was introduced. Initially, the original ResNet18 backbone of RT-DETR was modified by replacing the BasicBlock with a lightweight FasterNetBlock. Subsequently, the SimAM three-channel attention mechanism was integrated into the Neck part of RT-DETR to enhance the model’s accuracy. Finally, the GIoU loss function was replaced with the MPDIoU loss function to accelerate convergence and improve detection accuracy. Experimental results demonstrate that the improved RT-DETR algorithm achieved a mAP@0.5 of 94% on the contact lens circular ripple database, a 3.1% improvement over the original RT-DETR algorithm. Params and FLOPs were reduced by 15.6% and 13%, respectively, compared to the original algorithm. This algorithm effectively reduces computational complexity, enhancing the mean average precision of contact lens circular ripple defect detection. It holds promise for overcoming technical challenges in online detection of circular ripple defects in contact lenses. |
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