Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review

Saberironaghi, Alireza; Ren, Jing; El–Gindy, Moustafa

doi:10.3390/a16020095

Cited by 89 publications

(21 citation statements)

References 132 publications

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“…Deep learning, which can learn features directly from an original data, has achieved remarkable results in defect detection for industrial products [25,26], equipment fault diagnosis [27], and x-ray images-based COVID-19 detection [28]. Aslam et al [1] suggested that deep learning architectures can serve as a source of guidelines for designing and developing new solutions for leather defect inspection.…”

Section: Related Workmentioning

confidence: 99%

Comparative study on deep-learning-based leather surface defect identification

Chen,

Xu,

Deng

et al. 2023

Meas. Sci. Technol.

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Detecting leather surface defects has become an important subject in industrial inspections and attracted significant attention as a challenging task. Traditional image processing techniques struggle with the detection of leather surface defects with a variety of shapes, sizes, backgrounds, and noise. Deep learning is a promising solution to this problem. This work focuses on the comparative study of 26 classical deep learning models in the field of leather surface defect type recognition. That aims to lay a foundation for the design and development of new schemes for leather defect inspection. Based on tanned leather from an enterprise, eight types of leather surface defects (cavities, pinholes, scratches, rotten surfaces, growth lines, healing wounds, folds, and bacterial wounds) were collected using an ultra-high definition whole leather imaging device. Two challenging datasets with various shapes, sizes, and colours were constructed. Extensive experimental evaluations were conducted. The deep learning model can achieve more than 95% accuracy when the defect imaging is ideal and limited. In case that the shapes, sizes, and colour of the above eight defects keep diverse, Densenet169 performed the best with a recognition accuracy of 72.5%, and ShuffleNet model with the worst performance reached 64.3%. Systematic in-depth experimental evaluation shows that deep learning models are promising in the field of leather surface defect detection, however, challenges remain.

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Section: Related Workmentioning

confidence: 99%

Comparative study on deep-learning-based leather surface defect identification

Chen,

Xu,

Deng

et al. 2023

Meas. Sci. Technol.

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show abstract

“…In recent years, with the rapid development of deep learning, it has achieved significant breakthroughs in the field of computer vision [7]. Consequently, many researchers have proposed defect detection methods based on deep learning [8]. These methods utilize deep learning models such as convolutional neural networks (CNNs) to learn from a large number of images and achieve defect detection.…”

Section: Introductionmentioning

confidence: 99%

Deep learning cigarette defect detection method based on saliency feature guidance

Wang,

Chen,

et al. 2024

Electronics Letters

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Cigarette defect detection is important in industrial production. Existing methods extract features for defect detection manually or using deep learning. However, due to the small size of cigarette defects, these methods are unable to effectively extract discriminative features, limiting detection performance. Hence, a deep learning‐based method called significant feature‐guided cigarette defect detection (SFGCD) is proposed, which combines saliency feature extraction methods with deep learning to enhance feature representation and improve detection. First, edge saliency features are extracted using the proposed target gradient saliency feature extraction (GSFE) strategy. Then, a dense multi‐level feature fusion network is designed to combine the original features with the saliency features obtained from the target gradient saliency feature extraction strategy. This network enriches feature representation and improves detection by fusing original and saliency features at different levels and scales. Experimental results demonstrate that the proposed method achieves a higher accuracy of 0.02 mean average precision (MAP) value and a detection speed of 5 frames per second (FPS) on the authors' own labeled cigarette defect dataset compared to existing state‐of‐the‐art methods.

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“…1,6 If a model is trained on a specific defect dataset, it may not always produce the same accurate results on other defects datasets as the properties of the product surface may have different background colors or different types of defects. 7 Methods 8 have been developed that are trained on different datasets and use knowledge transfer to perform defect detection on other datasets; however, generic defect detection does not seems to work well for all types of defects.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for real-time wood plank detection and defect segmentation

et al. 2023

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Background: Defect detection and segmentation on product surfaces in industry has become one of the most important steps in quality control. There are many sophisticated hardware and software tools used in the industry for this purpose. The need for the real-time classification and detection of defects in industrial quality control has become a crucial requirement. Most algorithms and deep neural network architectures require expensive hardware to perform inference in real-time. This necessitates the design of architectures that are light-weight and suitable for deployment in industrial environments. Methods: In this study, we introduce a novel method for detecting wood planks on a fast-moving conveyor and using a convolutional neural network (CNN) to segment surface defects in real-time. A backbone network is trained with a large-scale image dataset. A dataset of 5000 images is created with proper annotation of wood planks and defects. In addition, a data augmentation technique is employed to enhance the accuracy of the model. Furthermore, we examine both statistical and deep learning-based approaches to identify and separate defects using the latest methods. Results: Our plank detection method achieved an impressive mean average precision of 97% and 96% of global pixel accuracy for defect segmentation. This remarkable performance is made possible by the real-time processing capabilities of our system, which can run at 30 frames per second (FPS) without sacrificing accuracy. Conclusions: The results of our study demonstrate the potential of our method not only in industrial wood processing applications but also in other industries where materials undergo similar processes of defect detection and segmentation. By utilizing our method, these industries can expect to see improved efficiency, accuracy, and overall productivity.

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Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review

Cited by 89 publications

References 132 publications

Comparative study on deep-learning-based leather surface defect identification

Comparative study on deep-learning-based leather surface defect identification

Deep learning cigarette defect detection method based on saliency feature guidance

Convolutional neural networks for real-time wood plank detection and defect segmentation

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