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%.
翻译:使用深层学习(DL) 的物体探测领域正在不断演变, 并提出了许多新的技术和模型。 YOLOv7 是一个基于YOLO系列模型的最新先进天体探测器, 以工业应用中流行的YOLO 模型为基础。 可能的应用领域之一是半导体或缺陷检查。 任何机器学习模型的性能都取决于其超参数。 此外, 以不同方式结合对一个或多个模型的预测也会影响性能。 在这个研究中, 我们实验了最近提出的YOLOv7, 即最新最先进的天体探测器, 培训和评价了具有不同超参数的模型, 以调查在半导体线空间模式缺陷的探测精度方面哪些模型改进了性能。 基础的YOLOv7 模型可以是默认超光度和不最大限制(NMS) 。 此外, 以平均精度( mAP ) 的垂直翻转图像在培训期间随机地显示所有缺陷模型的平均 AP 3 % 。 其它超光谱模型只用最精确的 AS IMB 的模型进行最佳的预测。