Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture

Ma, Zhuxi; Li, Yibo; Huang, Minghui; Huang, Qianbin; Cheng, Jie; Tang, Si Yuan

doi:10.1007/s10845-022-01930-3

Cited by 29 publications

(8 citation statements)

References 48 publications

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“…Wei et al [26] design an improved YOLOv3 with scale reduction and feature concatenation modules for detecting surface defects on fasteners and tracks. Ma et al [27] design a new backbone network to improve YOLOv4, and utilise the YOLOv4 to detect surface defects on aluminum strips. To accurately detect surface defects on steel, Zhao et al [28] improve YOLOv5 in three parts, including backbone, neck and head.…”

Section: One-stage Object Detection and Its Applicationmentioning

confidence: 99%

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NHD‐YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation

Chen,

Deng,

Gao

et al. 2024

IET Image Processing

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Surface defect detection is an essential task for ensuring the quality of products. Many excellent object detectors have been employed to detect surface defects in resent years, which has achieved outstanding success. To further improve the detection performance, a defect detector based on state‐of‐the‐art YOLOv8, named improved YOLOv8 by neck, head and data (NHD‐YOLO), is proposed. Specifically, YOLOv8 from three crucial aspects including neck, head and data is improved. First, a shortcut feature pyramid network is designed to effectively fuse features from backbone by improving the information transmission. Then, an adaptive decoupled head is proposed to alleviate the feature spatial misalignment between the classification and regression tasks. Finally, to enhance the training on small objects, a data augmentation method named selective small object copy and paste is proposed. Extensive experiments are conducted on three real‐world datasets: detection dataset from Northeastern University (NEU‐DET), printed circuit boards from Peking University (PKU‐Market‐PCB) and common objects in context (COCO). According to the results, NHD‐YOLO achieves the highest detection accuracy and exhibits outstanding inference speed and generalisation performance.

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Section: One-stage Object Detection and Its Applicationmentioning

confidence: 99%

“…Ma et al. [27] design a new backbone network to improve YOLOv4, and utilise the YOLOv4 to detect surface defects on aluminum strips. To accurately detect surface defects on steel, Zhao et al.…”

Section: Related Workmentioning

confidence: 99%

NHD‐YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation

Chen,

Deng,

Gao

et al. 2024

IET Image Processing

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show abstract

“…In addition, defects may have different shapes and be of different types. This causes errors in their classification and recognition since certain defects are similar in shape and structure [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Surface Illumination as a Factor Influencing the Efficacy of Defect Recognition on a Rolled Metal Surface Using a Deep Neural Network

Maruschak,

Konovalenko,

Osadtsa

et al. 2024

Applied Sciences

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Modern neural networks have made great strides in recognising objects in images and are widely used in defect detection. However, the output of a neural network strongly depends on both the training dataset and the conditions under which the image was acquired for analysis. We have developed a software–hardware method for evaluating the effect of variable lighting on the results of defect recognition using a neural network model. The proposed approach allows us to analyse the recognition results of an existing neural network model and identify the optimal range of illumination at which the desired defects are recognised most consistently. For this purpose, we analysed the variability in quantitative parameters (area and orientation) of damage obtained at different degrees of illumination for two different light sources: LED and conventional incandescent lamps. We calculated each image’s average illuminance and quantitative parameters of recognised defects. Each set of parameters represents the results of defect recognition for a particular illuminance level of a given light source. The proposed approach allows the results obtained using different light sources and illumination levels to be compared and the optimal source type/illuminance level to be figured out. This makes implementing a defect detection environment that allows the best recognition accuracy and the most controlled product quality possible. An analysis of a steel sheet surface showed that the best recognition result was achieved at an illuminance of ~200 lx. An illuminance of less than ~150 lx does not allow most defects to be recognised, whereas an illuminance larger than ~250 lx increases the number of small objects that are falsely recognised as defects.

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“…Qi et al [26] used MobileNet as the feature extractor of YOLOv3tiny to detect track fasteners in real time. Based on YOLOv4, Ma et al [27] conducted realtime detection of aluminum strip surface defects by embedding attention mechanism into the Ghost module. However, due to the detail capture ability of CNNs, their global modeling ability is insufficient.…”

Section: Introductionmentioning

confidence: 99%

Automatic pixel-level detection of tire defects based on a lightweight Transformer architecture

Sun

Liu

Zhai

et al. 2023

Meas. Sci. Technol.

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Efficient and accurate visual inspection of products is of great significance for intelligent manufacturing. In this paper, a visual inspection framework based on a lightweight Transformer is proposed for the pixel-level inspection of tire defects. A DP-Transformer feature encoder was proposed based on a hybrid architecture of convolutional neural network (CNN) and Transformer, which is used to learn the local and global relationship of defect features. Moreover, multi-scale fusion Transformer and spatial cross Transformer were proposed, based on which a feature decoder was built. In the decoder, the multi-scale fusion Transformer provides valuable spatial information for spatial cross Transformer, such that different levels of feature maps can refine the dependency of pixels through spatial cross Transformer. The proposed method is tested on the tire radiographic image dataset. Experimental results show that the proposed method reaches a detection accuracy of 98.57% and mIoU of 85.56%. Moreover, the method achieves the balance between accuracy and efficiency with satisfactory characterization ability for the defect geometric shape, and provides theoretical support for the future industrial deployment of transformer.

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Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture

Cited by 29 publications

References 48 publications

NHD‐YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation

NHD‐YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation

Surface Illumination as a Factor Influencing the Efficacy of Defect Recognition on a Rolled Metal Surface Using a Deep Neural Network

Automatic pixel-level detection of tire defects based on a lightweight Transformer architecture

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