ABSTRACT

Deep learning (DL) is an important enabling technology for intelligent manufacturing. The DL-based industrial image pattern recognition (DLBIIPR) plays a vital role in the improvement of product quality and production efficiency. Although DL technology has been widely used in the field of natural image, industrial image often has some mixed characteristics, such as small sample, imbalance, small target, strong interference, fine-grained, temporality and semantical, which reduce the feasibility and generalization of DLBIIPR. To solve this problem, this paper provides an overview of approaches commonly used in industry by enriching the sample space and limiting the hypothesis space. In order to improve the confidence of front-line workers in using DL models, the explainable deep learning (XDL) methods are reviewed, and a case study is used to verify the effectiveness of XDL.

摘要

深度学习（DL）是智能制造的重要使能技术。基于深度学习的工业图像模式识别 (DLBIIPR) 在提高产品质量和生产效率方面发挥着至关重要的作用。虽然深度学习技术在自然图像领域得到了广泛应用，但工业图像往往具有小样本、不平衡、小目标、强干扰、细粒度、时效性和语义等混合特征，降低了深度学习的可行性和泛化性。 DLBIPR。为了解决这个问题，本文通过丰富样本空间和限制假设空间，概述了工业界常用的方法。为了提高一线工作人员使用 DL 模型的信心，对可解释的深度学习 (XDL) 方法进行了回顾，