A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation

Li, Gang; Shao, Rui; Wan, Honglin; Zhou, Mingle; Li, Min

doi:10.1155/2022/9577096

Cited by 10 publications

(9 citation statements)

References 31 publications

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“…The approach used in this study is to divide the dataset into two groups: a training set and a test set. Refer to the following papers 56 – 58 , the network model is trained on about 70% of the data that are randomly selected, and the accuracy and robustness of the model are tested on the remaining 30% of the data, as shown in Table 1 . Many of the defects in the datasets have relatively modest sizes and diverse irregular shapes and patterns.…”

Section: Experiments and Analysismentioning

confidence: 99%

Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block

Zhang,

Li,

Miao

et al. 2024

Sci Rep

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To improve the precision of defect categorization and localization in images, this paper proposes an approach for detecting surface defects in hot-rolled steel strips. The approach uses an improved YOLOv5 network model to overcome the issues of inadequate feature extraction capacity and suboptimal feature integration when identifying surface defects on steel strips. The proposed method achieves higher detection accuracy and localization precision, making it more competitive and applicable in real production. Firstly, the multi-scale feature fusion (MSF) strategy is utilized to fuse shallow and deep features effectively and enrich detailed information relevant to target defects. Secondly, the CSPLayer Res2Attention block (CRA block) residual module is introduced to reduce the loss of defect information during hierarchical transmission, thereby enhancing the extraction of fine-grained features and improving the perception of details and global features. Finally, the experimental results indicate that the mAP on the NEU-DET and GC10-DET datasets approaches 78.5% and 67.3%, respectively, which is 4.9% and 2.1% higher than that of the baseline. Meanwhile, it has higher precision and more precise localization capabilities than other methods. Furthermore, it also achieves 59.2% mAP on the APDDD dataset, indicating its potential for growth in further domains.

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Section: Experiments and Analysismentioning

confidence: 99%

Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block

Zhang,

Li,

Miao

et al. 2024

Sci Rep

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“…Liu et al [24] presented an automatic synthetic tiny defect (small defects that are difficult to find at vertical angles) detection system for bearing surfaces with self-developed software and hardware. Li et al [25] proposed a surface defect detection model for industrial products based on attention enhancement.…”

Section: Related Workmentioning

confidence: 99%

Detection algorithm for bearing roller end surface defects based on improved YOLOv5n and image fusion

Xie¹,

Zhu²,

Luo³

et al. 2023

Meas. Sci. Technol.

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For the current problems of low accuracy and poor reliability of defect detection of bearing roller end surfaces in industrial production, this paper proposes a bearing roller end surface defect detection algorithm based on improved YOLOv5n and the fusion of gamma-corrected maps and curvature maps. First, this paper uses photometric stereo to reconstruct the three-dimensional shape of the surface and proposes an improved Frankot-Chellappa integration algorithm to solve the problem of reconstructing surface deformation. Secondly, the DenseFuse network is used to fuse gamma-corrected maps and curvature maps to generate an image dataset that combines the strengths of both images to enhance defect features and improve the precision of target detection. Finally, the improved target detection model YOLOv5n is proposed to detect defects in the end surfaces of bearing rollers. The experimental results show that using fused images for training, detection models with higher mAP than traditional images can be obtained, and the improved YOLOv5n algorithm maintains the high real-time performance of the original algorithm while the mean average precision mAP0.5 and mAP0.5:0.95 of improved YOLOv5n are 98.6% and 87.4% respectively, which are 0.9% and 2.8% higher than YOLOv5n respectively.

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“…In addition to its application to steel surface defects, it is also widely used in fabrics [31], wood [32] and other areas. Li and Shao [33] proposed an enhanced defect detection network using multi-head self-attention (MHSA), enabling better industrial product surface defect detection results. Zheng et al [34] presented a solution for surface defect detection based on YOLOv3.…”

Section: Application Development In Industrymentioning

confidence: 99%

AMFF-YOLOX: Towards an Attention Mechanism and Multiple Feature Fusion Based on YOLOX for Industrial Defect Detection

et al. 2023

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Industrial defect detection has great significance in product quality improvement, and deep learning methods are now the dominant approach. However, the volume of industrial products is enormous and mainstream detectors are unable to maintain a high accuracy rate during rapid detection. To address the above issues, this paper proposes AMFF-YOLOX, an improved industrial defect detector based on YOLOX. The proposed method can reduce the activation function and normalization operation of the bottleneck in the backbone network, and add an attention mechanism and adaptive spatial feature fusion within the feature extraction network to enable the network to better focus on the object. Ultimately, the accuracy of the prediction is enhanced without excessive loss of speed in network prediction, with competitive performance compared to mainstream detectors. Experiments show that the proposed method in this paper achieves 61.06% (85.00%) mAP@0.5:0.95 (mAP@0.5) in the NRSD-MN dataset, 51.58% (91.09%) is achieved in the PCB dataset, and 49.08% (80.48%) is achieved in the NEU-DET dataset. A large number of comparison and ablation experiments validate the effectiveness and competitiveness of the model in industrial defect detection scenarios.

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A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation

Cited by 10 publications

References 31 publications

Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block

Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block

Detection algorithm for bearing roller end surface defects based on improved YOLOv5n and image fusion

AMFF-YOLOX: Towards an Attention Mechanism and Multiple Feature Fusion Based on YOLOX for Industrial Defect Detection

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