ES-Net: Efficient Scale-Aware Network for Tiny Defect Detection

Yu, Xuyi; Lyu, Wentao; Zhou, Di; Wang, Chengqun; Xu, Weiqiang

doi:10.1109/tim.2022.3168897

Cited by 55 publications

(19 citation statements)

References 35 publications

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“…The data set used in the experiment were taken by Alibaba Cloud Tianchi [ 30 ] in a textile workshop in Guangdong Province. After manual sorting and selection, 10,321 pictures were selected, including 8 kinds of defects.…”

Section: Methodsmentioning

confidence: 99%

An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5

Lin

Liu

Xia

et al. 2022

Sensors

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Limited by computing resources of embedded devices, there are problems in the field of fabric defect detection, including small defect size, extremely unbalanced aspect ratio of defect size, and slow detection speed. To address these problems, a sliding window multihead self-attention mechanism is proposed for the detection of small targets, and the Swin Transformer module is introduced to replace the main module in the original YOLOv5 algorithm. First, to reduce the distance between several scales, the weighted bidirectional feature network is employed on embedded devices. In addition, it is helpful to improve the perception of small-target faults by incorporating a detection layer to achieve four-scale detection. At last, to improve the learning of positive sample instances and lower the missed detection rate, the generalized focal loss function is finally implemented on YOLOv5. Experimental results show that the accuracy of the improved algorithm on the fabric dataset reaches 85.6%, and the mAP is increased by 4.2% to 76.5%, which meets the requirements for real-time detection on embedded devices.

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Section: Methodsmentioning

confidence: 99%

An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5

Lin

Liu

Xia

et al. 2022

Sensors

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“…Zhang [30] improved the original YOLOv3 by introducing a new migration learning method for detecting concrete bridge defects, and its performance was improved by 13% compared to the original YOLOv3. Yu [31] improved YOLOv4-CSP based on the problem of small targets for industrial defect detection. They proposed an efficient stepped pyramidal network for fusing multi-scale features, thus improving the detection accuracy of small objects.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

LFF-YOLO: A YOLO Algorithm With Lightweight Feature Fusion Network for Multi-Scale Defect Detection

et al. 2022

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The detection of defects is indispensable in industrial production. Surface defects have different scales. Both minimal flaws and significant scratches may appear on the same product. The standard method uses a multi-scale feature fusion network, introducing many parameters that may reduce the inference speed. In actual industrial production scenarios, inference speed and accuracy play an equally important role. Therefore we propose an algorithm to effectively improve the detection speed while improving the detection accuracy. The model proposed in this paper called "YOLO with lightweight feature fusion network(LFF-YOLO)." First, we use ShuffleNetv2 as a feature extraction network to reduce the number of parameters. Then, to improve the efficiency of multi-scale feature fusion, we propose the lightweight feature pyramid network (LFPN). Considering that the fixed receptive field is difficult to adapt to the defects of different scales, it may lead to the difficulty of model convergence and seriously affect the detection performance. Therefore, we propose the adaptive receptive field feature extraction (ARFFE) module, which weights the multi-receptive field channels to generate multi-receptive field information. In addition, focal loss is used to solve the problem of imbalance between positive and negative samples. Finally, we conducted experiments on NEU-DET (79.23% mAP), Peking University printed circuit board defect dataset (93.31% mAP),and GC10-DET (59.78% mAP), respectively. Extensive experiments show that our proposed method achieves optimal detection speed compared with the prevailing methods, and the detection accuracy of our method is also highly competitive.

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“…With the rise of deep learning, numerous works apply generalized computer vision methods for defect detection. Some works are based on object detection [13,14,28,30,31], which relies on annotated rectangular boxes, enabling locating and classifying defects end-to-end. The other is applying semantic segmentation [5,18,24], which enables pixellevel localization, suit for complex scenarios with difficultto-locate boundaries.…”

Section: Deep Learning-based Defect Localizationmentioning

confidence: 99%

PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow

Lei¹,

Wang²,

Liu³

2023

Preprint

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During industrial processing, unforeseen defects may arise in products due to uncontrollable factors. Although unsupervised methods have been successful in defect localization, the usual use of pre-trained models results in lowresolution outputs, which damages visual performance. To address this issue, we propose PyramidFlow, the first fully normalizing flow method without pre-trained models that enables high-resolution defect localization. Specifically, we propose a latent template-based defect contrastive localization paradigm to reduce intra-class variance, as the pre-trained models do. In addition, PyramidFlow utilizes pyramid-like normalizing flows for multi-scale fusing and volume normalization to help generalization. Our comprehensive studies on MVTecAD demonstrate the proposed method outperforms the comparable algorithms that do not use external priors, even achieving state-of-the-art performance in more challenging BTAD scenarios.

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ES-Net: Efficient Scale-Aware Network for Tiny Defect Detection

Cited by 55 publications

References 35 publications

An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5

An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5

LFF-YOLO: A YOLO Algorithm With Lightweight Feature Fusion Network for Multi-Scale Defect Detection

PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow

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