为半导体缺陷检测优化 YOLOv7,arXiv - CS - Computer Vision and Pattern Recognition

当前位置： X-MOL 学术 › arXiv.cs.CV › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

为半导体缺陷检测优化 YOLOv7
arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2023-02-19 , DOI: arxiv-2302.09565
Enrique Dehaerne, Bappaditya Dey, Sandip Halder, Stefan De Gendt

随着许多新技术和模型的提出，使用深度学习 (DL) 的对象检测领域不断发展。YOLOv7 是基于 YOLO 系列模型的最先进的物体检测器，这些模型已在工业应用中广受欢迎。一种可能的应用领域是半导体缺陷检测。任何机器学习模型的性能都取决于它的超参数。此外，以不同方式组合一个或多个模型的预测也会影响性能。在这项研究中，我们使用最近提出的最先进的物体检测器 YOLOv7 进行实验，通过训练和评估具有不同超参数的模型来研究哪些模型可以提高半导体线空间图案缺陷的检测精度。具有默认超参数和非最大抑制 (NMS) 预测组合的基础 YOLOv7 模型在平均平均精度 (mAP) 方面优于之前工作的所有 RetinaNet 模型。我们发现在训练期间随机垂直翻转图像会使所有缺陷类别的平均 AP 提高 3%。与默认模型相比，其他超参数值仅针对某些类别改进了 AP。发现将针对不同缺陷类别实现最佳 AP 的模型组合起来是一种有效的集成策略。使用加权框融合 (WBF) 预测结合来自集合的预测给出了最佳性能。WBF 的最佳集成在默认模型的 mAP 上提高了 10%。

"点击查看英文标题和摘要"

Optimizing YOLOv7 for Semiconductor Defect Detection

The field of object detection using Deep Learning (DL) is constantly evolving with many new techniques and models being proposed. YOLOv7 is a state-of-the-art object detector based on the YOLO family of models which have become popular for industrial applications. One such possible application domain can be semiconductor defect inspection. The performance of any machine learning model depends on its hyperparameters. Furthermore, combining predictions of one or more models in different ways can also affect performance. In this research, we experiment with YOLOv7, a recently proposed, state-of-the-art object detector, by training and evaluating models with different hyperparameters to investigate which ones improve performance in terms of detection precision for semiconductor line space pattern defects. The base YOLOv7 model with default hyperparameters and Non Maximum Suppression (NMS) prediction combining outperforms all RetinaNet models from previous work in terms of mean Average Precision (mAP). We find that vertically flipping images randomly during training yields a 3% improvement in the mean AP of all defect classes. Other hyperparameter values improved AP only for certain classes compared to the default model. Combining models that achieve the best AP for different defect classes was found to be an effective ensembling strategy. Combining predictions from ensembles using Weighted Box Fusion (WBF) prediction gave the best performance. The best ensemble with WBF improved on the mAP of the default model by 10%.

更新日期：2023-02-21

点击分享查看原文

点击收藏

阅读更多本刊新发论文