A dual-branch balance saliency model based on discriminative feature for fabric defect detection

Liu, Zhoufeng; Wang, Menghan; Li, Chunlei; Ding, Shuai; Li, Bicao

doi:10.1108/ijcst-02-2021-0017

Cited by 6 publications

(5 citation statements)

References 46 publications

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“…Many deep learning-based saliency approaches have emerged and have been successfully introduced to the fabric defect detection field. Liu et al 40 introduced a dual-branch balanced saliency model to enhance the discrimination of the fabric defect features and further refine the predicted saliency maps. Wang et al 42 presented a deep learning-based SOD model incorporating a self-attentive mechanism into CNNs for fabric defect detection.…”

Section: Saliency-based Fabric Defect Detection Methodsmentioning

confidence: 99%

“…Liu et al. 40 introduced a dual-branch balanced saliency model to enhance the discrimination of the fabric defect features and further refine the predicted saliency maps. Wang et al.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the effective aggregation of low-high-level features is very important for fabric defect detection. Recently, many saliency-based works 40,41 have utilized various sophisticated modules and structures sufficiently to capture and aggregate multi-level features for fabric defect detection, achieving good performance. However, these still suffer from two main challenges in the fabric defect detection field.…”

mentioning

confidence: 99%

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CACFNet: Fabric defect detection via context-aware attention cascaded feedback network

Liu

Tian

et al. 2023

Textile Research Journal

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Fabric defect detection plays an irreplaceable role in the quality control of the textile manufacturing industry, but it is still a challenging task due to the diversity and complexity of defects and environmental factors. Visual saliency models imitating the human vision system can quickly determine the defect regions from the complex texture background. However, most visual saliency-based methods still suffer from incomplete predictions owing to the variability of fabric defects and low contrast with the background. In this paper, we develop a context-aware attention cascaded feedback network for fabric defect detection to achieve more accurate predictions, in which a parallel context extractor is designed to characterize the multi-scale contextual information. Moreover, a top-down attention cascaded feedback module was devised adaptively to select the important multi-scale complementary information and then transmit it to an adjacent shallower layer to compensate for the inconsistency of information among layers for accurate location. Finally, a multi-level loss function is applied to guide our model for generating more accurate prediction results via optimizing multiple side-output predictions. Experimental results on the two fabric datasets built under six widely used evaluation metrics demonstrate that our proposed framework outperforms state-of-the-art models remarkably.

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Section: Saliency-based Fabric Defect Detection Methodsmentioning

confidence: 99%

“…Liu et al. 40 introduced a dual-branch balanced saliency model to enhance the discrimination of the fabric defect features and further refine the predicted saliency maps. Wang et al.…”

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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CACFNet: Fabric defect detection via context-aware attention cascaded feedback network

Liu

Tian

et al. 2023

Textile Research Journal

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“…Convolutional neural network (CNN) with powerful feature extraction capability has been successfully applied into the object detection, image segmentation and image restoration. And the CNN-based salient object detection methods (SODs) [11]- [14] have attracted much attention and have been applied into the fabric detection [15]- [17], but these methods mainly concentrate on devising sophisticated architectures to aggregate convolutional features of multiple layers, which are not consider how to extract powerful and discriminant multi-scale features via designing an effective extractor or how to better take advantage of the features of different levels and scales. In addition, these methods usually cannot generate the refined boundaries.…”

Section: Introductionmentioning

confidence: 99%

Saliency-based fabric defect detection network with feature pyramid learning and refinement

Liu¹,

Tian²,

Li³

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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Fabric defect detection is an essential step of quality control in the textile manufacturing industry. The fabric image texture and defects are complex and diverse, which result in poor detection results and low efficiency of the traditional fabric defect detection algorithm. Visual saliency model can quickly outstand the salient object from the complex background, and has been proven applicable in fabric defect detection. However, the existing saliency detection models still confront great challenges in boundary refinement and line-shaped defect detection. Therefore, a novel saliency-based fabric defect detection network with feature pyramid learning and refinement module is proposed to powerfully characterize features and refine boundary, in which a scale-correlated feature pyramid module (SCFPM) with cross-level connections is proposed to effectively characterize the multi-scale features from the backbone network. Moreover, an auxiliary refinement module (ARM) is designed to further refine and strengthen the input features. Finally, we incorporated the hand-crafted saliency priors to guide the network to generate the accurate saliency maps. Extensive experiments on the built fabric image datasets demonstrate that our proposed model outperforms most state-of-the-art methods under different evaluation metrics.

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“…One is the direction based on traditional image processing, and the other is the direction based on deep learning image detection. In traditional image processing, Liu et al [9] proposed a two-branch balanced saliency model based on discriminant features for fabric defect detection. This method can be used for accurate fabric defect detection and even surface defect detection for other industrial products.…”

Section: Introductionmentioning

confidence: 99%

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

Meng,

Cui,

Liu

et al. 2023

Processes

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Under the background of intelligent manufacturing, in order to solve the complex problems of manual detection of metallurgical saw blade defects in enterprises, such as real-time detection, false detection, and the detection model being too large to deploy, a study on a metallurgical saw blade surface defect detection algorithm based on SC-YOLOv5 is proposed. Firstly, the SC network is built by integrating coordinate attention (CA) into the Shufflenet-V2 network, and the backbone network of YOLOv5 is replaced by the SC network to improve detection accuracy. Then, the SIOU loss function is used in the YOLOv5 prediction layer to solve the angle problem between the prediction frame and the real frame. Finally, in order to ensure both accuracy and speed, lightweight convolution (GSConv) is used to replace the ordinary convolution module. The experimental results show that the mAP@0.5 of the improved YOLOv5 model is 88.5%, and the parameter is 31.1M. Compared with the original YOLOv5 model, the calculation amount is reduced by 56.36%, and the map value is increased by 0.021. In addition, the overall performance of the improved SC-YOLOv5 model is better than that of the SSD and YOLOv3 target detection models. This method not only ensures the high detection rate of the model, but also significantly reduces the complexity of the model and the amount of parameter calculation. It meets the needs of deploying mobile terminals and provides an effective reference direction for applications in enterprises.

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A dual-branch balance saliency model based on discriminative feature for fabric defect detection

Cited by 6 publications

References 46 publications

CACFNet: Fabric defect detection via context-aware attention cascaded feedback network

CACFNet: Fabric defect detection via context-aware attention cascaded feedback network

Saliency-based fabric defect detection network with feature pyramid learning and refinement

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

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