As demand for intelligent manufacturing continues to grow, tiny object quality assessment (TOQA) is becoming increasingly importance in industrial automation. Recently, visual‐based TOQA has attracted an increasing attention, since the physical appearance is the foremost assessment index for evaluating the tiny object quality. It is exhausted and challenging to determine the quality of tiny object by manual visual inspection, and thus some machine vision systems are developed for automatic TOQA. Existing systems often use a limited number of cameras to capture the image of fallen tiny object, and thus may be not reliable since the tiny object may be unsound (such as cracked or damaged) in an invisible side. In this article, we develop a novel system for automatic TOQA that captures images of tiny object from multiple (more than two) view points, and propose a novel method termed weighted ensemble network (WENet) to effectively integrate the information of different views. In particular, convolutional neural networks (CNNs) are adopted to extract features from the images of different views. Then the multiview features are weighted combined for tiny object quality prediction. Traditional ensemble approaches usually directly applying average or voting to the prediction results of different views, or learn fixed weights to combine the results. Different from these approaches, the weights are adaptively determined in our method according to the quality of the captured image, since the features extracted from a low‐quality (e.g., blurred) image should contribute less to the final prediction. Handcrafted features and deep features are integrated in a sophisticated way in our method, and we empirically demonstrate the effectiveness of our method on grain quality assessment by investigating different CNN architectures for feature extraction and comparing with the conventional ensemble approaches.

中文翻译：

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加权集成网络用于基于多视图的微小对象质量评估

随着对智能制造的需求不断增长，微小物体质量评估（TOQA）在工业自动化中变得越来越重要。近年来，基于视觉的TOQA引起了越来越多的关注，因为物理外观是评估微小物体质量的首要评估指标。通过手动视觉检查来确定微小物体的质量是疲惫不堪和具有挑战性的，因此开发了一些用于自动TOQA的机器视觉系统。现有系统通常使用有限数量的相机来捕获下落的微小物体的图像，因此可能不可靠，因为微小物体在不可见的一侧可能不健全（例如破裂或损坏）。在本文中，我们开发了一种新颖的自动TOQA系统，可以从多个（两个以上）视点捕获微小物体的图像，并提出了一种称为加权集成网络（WENet）的新方法，以有效地整合不同视图的信息。特别是，采用卷积神经网络（CNN）从不同视图的图像中提取特征。然后，对多视图特征进行加权组合，以进行微小的对象质量预测。传统的集成方法通常直接将平均或投票应用于不同视图的预测结果，或者学习固定权重以合并结果。与这些方法不同，权重是根据捕获图像的质量自适应确定的，因为从低质量（例如，模糊）图像中提取的特征对最终预测的贡献较小。在我们的方法中，以复杂的方式将手工制作的功能和深度功能集成在一起，