Review of Industry Workpiece Classification and Defect Detection using Deep Learning (original) (raw)
Related papers
IEEE Access, 2022
Currently, the development of industry is becoming increasingly rapid. Technicalization, informatization and industrialization give the fundamental impetus for industrial development and progress. Nevertheless, there are numerous problems that are hindering industrial progress and threatening human security in the industrial field. The surface defects of the workpieces are one of the primary problems. Moreover, defects of multi-type, mixed and unapparent characteristics presented by workpieces make the detection and classification of workpiece more difficult. Deep convolutional neural networks (DCNN) show strong ability of feature extraction and mines deeper essential features of data because of its features of unique receptive field structure and weights of shared. It can represent original data information well and obtain results more accurately than the traditional methods. But there also remains a problem that conventional DCNN has a huge number of parameters and computation, which brings great pressure to the equipment in terms of computing power, memory, speed and so on. Based on this situation, the optimization methods of CNNs model in the aspects of data, structure, algorithm are summarized. Related lightweight structures and networks are also summarized in this paper. The purpose of these work is to reduce the number of parameters and computation and improve the training performance. At the same time, the research on defect classification of workpieces based on traditional machine learning and deep learning model is reviewed, and the research on defect classification of workpieces based on deeply optimized CNNs is referred and prospected.
Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey
IEEE Access
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.
Defect Detection in Manufacturing: An Integrated Deep Learning Approach
Journal of Computer and Communications, 2024
This research investigates deep learning-based approach for defect detection in the steel production using Severstal steel dataset. The developed system integrates DenseNet121 for classification and DeepLabV3 for segmentation. DenseNet121 achieved high accuracy with defect classification as it achieved 92.34% accuracy during testing. This model significantly outperformed benchmark models like VGG16 and ResNet50, which achieved 72.59% and 92.01% accuracy, respectively. Similarly, for segmentation, DeepLabV3 showed high performance in localizing and categorizing defects, achieving a Dice coefficient of 84.21% during training and 69.77% during validation. The dataset includes steels which have four different types of defects and the DeepLab model was particularly effective with detection of Defect 4, with a Dice coefficient of 87.69% in testing. The model performs suboptimally in segmentation of Defect 1, achieving an accuracy of 64.81%. The overall system's integration of classification and segmentation, alongside thresholding techniques, resulted in improved precision (92.31%) and reduced false positives. Overall, the proposed deep learning system achieved superior defect detection accuracy and reliability compared to existing models in the literature.
IRJET, 2021
To improve manufacturing of quality products and decrease production cost, image processing and machine learning based techniques are widely used nowadays. Advances in Artificial Intelligence technologies which provides advanced tools to inspect products, analyzed the quality of products, helps us to improved manufacturing quality and quantity. However, to improve quality of products, proper inspection of manufacturing defects is crucial. Therefore, in this study, we discuss the existing image processing, computer vision and machine learning techniques that are often used to detect defects in the products and help to inspects them effectively. Deep learning based state-of-the-art studies are presented and compared. We also highlighted the benefits and limitation of existing algorithms which may help researchers to find a better solution such challenges. Finally, paper summarize the study by discussing most efficient approach for defect detection in manufacturing process.
Metal Defect Classification Using Deep Learning
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN), 2021
In the era of Industry 4.0, the vast development of Smart Factory is always followed by the advancement of Deep Learning technology. To avoid the smart factory system from unwanted losses because of defects in its output production in the steel factory, defect classification on steel sheets based on Deep Learning should be developed precisely. This paper explains how the Deep Learning technique was used to implement defect detection in a smart factory. For this study, we used an open dataset of steel defects. The result of the Deep Learning method for the defect detection system generates 96% accuracy, 0.95 recall, and a precision of 0.97 on the training process. This research goal may contribute to enhancing efficiency and cost reduction in the smart steel factory environment.
Deep-Learning-Based Computer Vision System for Surface-Defect Detection
Lecture Notes in Computer Science, 2019
Automating optical-inspection systems using machine learning has become an interesting and promising area of research. In particular, the deeplearning approaches have shown a very high and direct impact on the application domain of visual inspection. This paper presents a complete inspection system for automated quality control of a specific industrial product. Both hardware and software part of the system are described, with machine vision used for image acquisition and pre-processing followed by a segmentation-based deep-learning model used for surface-defect detection. The deep-learning model is compared with the state-of-the-art commercial software, showing that the proposed approach outperforms the related method on the specific domain of surface-crack detection. Experiments are performed on a real-world quality-control case and demonstrate that the deep-learning model can be successfully used even when only 33 defective training samples are available. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited.
A review on modern defect detection models using DCNNs – Deep convolutional neural networks
Journal of Advanced Research, 2021
h i g h l i g h t s A comprehensive analysis of modern object detection models Study on models that can be used as detectors for defect detection applications in industry. Study on YOLOv4 that can perform good defect detection with not much capital investment. Analysis on the correlation between dataset, labeling and the data augmentation steps and accuracy and computations. Analysis on the importance of correct data acquiring, augmentation and labeling in low cost applications. Analysis of the rate of improvement of the mAP of defect detection and image classification systems in recent years. Analysis of model compression and acceleration on embedded applications and smart factories.
Multiple Object Detection of Workpieces Based on Fusion of Deep Learning and Image Processing
2020 International Joint Conference on Neural Networks (IJCNN), 2020
A workpiece detection method based on fusion of deep learning and image processing is proposed. Firstly, the workpiece bounding boxes are located in the workpiece images by YOLOv3, whose parameters are compressed by an improved convolutional neural network residual structure pruning strategy. Then, the workpiece images are cropped based on the bounding boxes with cropping biases. Finally, the contours and suitable gripping points of the workpieces are obtained through image processing. The experimental results show that mean Average Precision (mAP) is 98.60% for YOLOv3, and 99.38% for that one by pruning 50.89% of its parameters, and the inference time is shortened by 31.13%. Image processing effectively corrects the bounding boxes obtained by deep learning, and obtains workpiece contour and gripping point information.
Research on Steel Surface Defect Detection Algorithm Based on Improved Deep Learning
IJEER , 2022
With the development of industrial economy, more and more enterprises use machine vision and artificial intelligence to replace manual detection. Therefore, the research of steel surface defect detection based on artificial intelligence is of great significance to promote the rapid development of intelligent factory and intelligent manufacturing system. In this paper, Yolov5 deep learning algorithm is used to build a classification model of steel surface defects to realize the classification and detection of steel surface defects. At the same time, on the basis of Yolov5, combined with the attention mechanism, the backbone network is improved to further improve the classification model of steel surface defects. The experiment shows that the Recall and mAP of improved Yolov5 perform better on the steel surface defect data set. Compared with Yolov5, the number of C3CA-Yolov5 parameters decreased by 13.02%, and the size of pt files decreased by 12.72%; the number of C3ECA-Yolov5 parameters decreased by 13.36%, and the size of pt files decreased by 13.22%.
Future Internet, 2021
Deep learning provides new ways for defect detection in automatic optical inspections (AOI). However, the existing deep learning methods require thousands of images of defects to be used for training the algorithms. It limits the usability of these approaches in manufacturing, due to lack of images of defects before the actual manufacturing starts. In contrast, we propose to train a defect detection unsupervised deep learning model, using a much smaller number of images without defects. We propose an unsupervised deep learning model, based on transfer learning, that extracts typical semantic patterns from defect-free samples (one-class training). The model is built upon a pre-trained VGG16 model. It is further trained on custom datasets with different sizes of possible defects (printed circuit boards and soldered joints) using only small number of normal samples. We have found that the defect detection can be performed very well on a smooth background; however, in cases where the de...