Autofocus Dataset

In industrial microscopic detection, learning-based autofocus methods have empowered operators to acquire high-quality images quickly. Learning-based methods consist of two parts: network model and prior dataset. The network model, which approximates the relationship between input and output, exists fitting error. The prior dataset is made by sharpness metric and used for model training, while the limitations of the metric itself will affect the accuracy of the dataset. Both the model and dataset are prone to errors, thereby limiting the potential for further improvements in focusing accuracy. In this paper, a high-precision autofocus pipeline was introduced, which predicts the defocus distance from a single natural image. A new method for making datasets was proposed, which overcomes the limitations of the sharpness metric itself and improves the overall accuracy of the dataset. Furthermore, a lightweight regression network was built, namely Natural-image Defocus Prediction Model (NDPM), to improve the focusing accuracy. A realistic dataset of sufficient size was made to train all models. The experiment shows NDPM has better focusing performance compared with other models, with a mean focusing error of 0.422μm.  在工业显微检测领域，基于学习的自动对焦方法已赋能操作人员快速获取高质量图像。此类基于学习的方法主要包含两大组成部分：网络模型与先验数据集。用于拟合输入与输出映射关系的网络模型存在拟合误差。先验数据集通过清晰度评价指标（sharpness metric）构建并用于模型训练，但该指标自身的局限性会对数据集的精度造成负面影响。模型与数据集均易引入误差，进而制约了对焦精度进一步提升的潜力。本文提出了一种高精度自动对焦流程，可通过单幅自然图像预测离焦距离。同时，本文提出了一种全新的数据集构建方法，可克服清晰度评价指标本身的局限性，有效提升数据集的整体精度。此外，为进一步提升对焦精度，本文构建了一款轻量级回归网络，命名为自然图像离焦预测模型（Natural-image Defocus Prediction Model，NDPM）。本文构建了规模充足的真实场景数据集，用于所有模型的训练。实验结果表明，相较于其他对比模型，NDPM的对焦性能更优，平均对焦误差仅为0.422μm。

搜集汇总

背景与挑战

背景概述

Autofocus Dataset是一个用于工业显微镜检测的高精度自动对焦数据集，旨在通过新的数据集制作方法克服传统清晰度度量的限制，提高对焦准确性。该数据集支持训练轻量级回归网络NDPM，实验证明其平均对焦误差为0.422μm，优于其他模型。