Automated Surface Defect Detection in Metals: A Comparative Review of Object Detection and Semantic Segmentation Using Deep Learning

Usamentiaga, Rubén; Lema, Darío G.; Pedrayes, Oscar D.; Daniel, Garcia

doi:10.1109/tia.2022.3151560

Cited by 72 publications

(15 citation statements)

References 21 publications

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“…For instance, in semantic segmentation, the encoder captures contextual information from input images, and the decoder generates pixel-wise class predictions [50,51]. This approach has demonstrated success in diverse fields, such as medical image segmentation [52], scene understanding [53], and object detection [54]. Depending on the specific application, the encoder and decoder components can be chosen from a variety of deep learning models, including CNNs [37], RNNs [42], and LSTM [44].…”

Section: Mude-cnnmentioning

confidence: 99%

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Rezasefat,

Hogan

2024

Mach. Learn.: Sci. Technol.

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This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the Multi-Decoder CNN (MUDE-CNN) and the Multiple Encoder-Decoder Model with Transfer Learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder-decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via additive manufacturing. The temporal model evaluation demonstrated MTED-TL's consistent superiority over MUDE-CNN, owing to transfer learning's advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.

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Section: Mude-cnnmentioning

confidence: 99%

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Rezasefat,

Hogan

2024

Mach. Learn.: Sci. Technol.

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“…The GhostBottlenet module reduced the computation by approximately 36% compared with that of the YOLOv5s model and improved performance by approximately 2%. Usamentiaga et al [11] applied a deep-learning-based machine vision inspection system to detect surface defects in steel. Here, YOLOv5 and U-Net, representative computer vision algorithms, were applied to the study.…”

Section: Related Researchmentioning

confidence: 99%

A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning

Noh

Jang

Kim³

et al. 2023

Applied Sciences

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With increasing interest in smart factories, considerable attention has been paid to the development of deep-learning-based quality inspection systems. Deep-learning-based quality inspection helps productivity improvements by solving the limitations of existing quality inspection methods (e.g., an inspector’s human errors, various defects, and so on). In this study, we propose an optimized YOLO (You Only Look Once) v5-based model for inspecting small coils. Performance improvement techniques (model structure modification, model scaling, pruning) are applied for model optimization. Furthermore, the model is prepared by adding data applied with histogram equalization to improve model performance. Compared with the base model, the proposed YOLOv5 model takes nearly half the time for coil inspection and improves the accuracy of inspection by up to approximately 1.6%, thereby enhancing the reliability and productivity of the final products.

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“…A comparison between semantic segmentation and object detection is showed in [13]. YOLOv5 and UNet are two popular object algorithms that are utilized with the NEU dataset.…”

Section: Introductionmentioning

confidence: 99%

Enhancing automated inspection in metal industries: zero-shot segmentation of surface defects using bounding box prompts

Lema,

Usamentiaga,

García

2024

Meas. Sci. Technol.

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Surface defect detection poses a challenging problem that has been addressed for decades. Many of actual solutions are based on deep learning algorithms.However, these algorithms require a large amount of data to train accurate models. This becomes especially problematic for semantic segmentation algorithms, which need labelled datasets at the pixel level, a laborious and time-consuming task. Therefore, this paper proposes a semi-supervised method in which the predictions of an object detector are combined with the segmentation of a zero-shot model, eliminating the need to label a dataset for semantic segmentation. Results are compared with relevant supervised semantic segmentation models, such as UNet and DeepLabv3+. UNet achieves an F1 score of 0.824, while DeepLabv3+ achieves 0.847. The proposed method combining YOLOv8n and Segment Anything Model (SAM) achieves an F1 score of 0.804. This slight loss of F1 is compensated by the signiﬁcant time savings, unlike UNet and DeepLabv3+, where labelling of the dataset is necessary.

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Automated Surface Defect Detection in Metals: A Comparative Review of Object Detection and Semantic Segmentation Using Deep Learning

Cited by 72 publications

References 21 publications

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning

Enhancing automated inspection in metal industries: zero-shot segmentation of surface defects using bounding box prompts

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