Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Abstract

Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.

Intelligent IoT framework with GAN‐synthesized images for enhanced defect detection in manufacturing

“…The deep learning (DL) approach has been considered for manufacturing defect detection during the past ten years. The number of research publications on DL‐based defect detection has also steadily increased 26 . The DL technique can be classified into supervised, unsupervised, and weakly supervised methods.…”

A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors

“…The recognition of human actions in a workflow targeting HAR requires a pipeline entailing a sequence of steps that may include data processing, feature extraction and artificial intelligence (AI) techniques to perform classification: at first, the signals acquired from sensors are processed to reduce noise [41], cope with missing values and remove possible artifacts [9,42]; secondly, data are segmented to identify the portion of the preprocessed signals that are informative of the executed activities [43]; signals can be optionally converted into images as well [15,17,20,28,44,45]; afterward, features are extracted for each segment from either images or time-series data [1,2,38,46] to capture meaningful characteristics of the performed activities; ultimately, these features and their corresponding ground truth labels are used as input to train a classifier, whose performance is evaluated based on quantitative criteria, such as accuracy [47].…”

“…On the one hand, Machine Learning (ML) procedures are trained on hand-crafted features [24,29], but implies a manual extraction based on domain knowledge that can be increasingly time-consuming as the dataset dimensionality enlarges due to the need for the high amount of repetitions and subjects for the sake of generalizability [28]. On the other side, Deep Learning (DL) architectures can be directly fed by raw data and automatically learn patterns through the process of backpropagation without any prior knowledge of the signals [47].…”

Deep convolutional neural network for weld defect classification in radiographic images

“…The increase in industrial production brings with it the need to test a large volume of samples in a short period, so the fatigue of the worker and the subjectivity of the evaluations make this process unreliable [ 12 ]. Due to this, new advanced computational techniques are necessary to improve the quality and efficiency of the welded joint inspection process [ [13] , [14] , [15] , [16] , [17] ].…”