An Improved Fabric Defect Detection Method Based on                     SSD

Xie, Huosheng; Zhang, Yafeng; Wu, Zesen

doi:10.14504/ajr.8.s1.22

Cited by 14 publications

(14 citation statements)

References 37 publications

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“…Among them, Xie et al. 29 proposed an improved fabric defect detection method based on SSD, Wei et al. 30 developed a methodology based on the FPN approach to detect multi-class fabric defects, and Liu et al.…”

Section: Resultsmentioning

confidence: 99%

“…We also compared the single defect detection results with those of other methods. Among them, Xie et al 29 proposed an improved fabric defect detection method based on SSD, Wei et al 30 developed a methodology based on the FPN approach to detect multi-class fabric defects, and Liu et al 31 proposed an improved YOLOv4 fabric defect detection method. According to Table 5, our model performs better than these methods on most single flaws, such as broken warps and jumping flower.…”

Section: Comparative Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

Defect detection algorithm for fabric based on deformable convolutional network

Luo

Cheng

et al. 2022

Textile Research Journal

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The detection of fabric defects is an important aspect of textile quality management. This paper proposes an algorithm based on YOLOv3 and deformable convolutional network to solve the problems of low accuracy, high rate of missed and false detection, and high labor cost existing in traditional manual detection methods. First, data enhancement is adopted to address the problem of imbalanced categories of defective samples in the dataset. Then, the Resnet101 model is used to extract the features, and the original convolution operation is replaced by deformable convolution to balance the accuracy and speed of the model. Finally, the focal loss function is introduced to solve the insensitivity problem of the model to difficult defect samples. Experimental results show that this method can quickly and accurately detect defects on the surface of fabrics. In real-time detection, the average accuracy of this method is 8.3% higher than that of YOLOv3. The average accuracy of multiple categories of detection reaches more than 90%, and the detection effect of small defect targets, such as three silk, is also relatively better.

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Section: Resultsmentioning

confidence: 99%

Section: Comparative Experimentsmentioning

confidence: 99%

Defect detection algorithm for fabric based on deformable convolutional network

Luo

Cheng

et al. 2022

Textile Research Journal

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show abstract

“…The recurrent neural network (CRF-RCNN) proposed in [26] is a two-stage extractor combining bilateral convolutional networks and conditional random fields, which helps to smooth out constraints or obtain fine-grained inspection results. An improved single-shot multibox detector (SSD) is proposed in [27], which adds a full convolutional compression and excitation (FCSE) module. The attentional neural network based on joint intersection consistency (IoU)-guided centroid estimation (CCEANN) proposed in [28] achieves high accuracy in defect detection.…”

Section: Introductionmentioning

confidence: 99%

Detection of Surface Defects on Railway Tracks Based on Deep Learning

Wang

Zhu

et al. 2022

IEEE Access

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The detection of rail surface defects is very important in railway transportation. However, the edge defects on both sides of the rail and the multi-scale variation between different types of defects both pose challenges to the detection of rail surface defects. In order to solve the above problems, this paper proposes a novel rail surface defect detection network, YOLOv5s-VF. First, we design a sharpening functional attention mechanism (V-CBAM) that contains two key components: adaptive channel attention (F-CAM) and sharpened spatial attention (SSA). In F-CAM, we use one-dimensional convolution with adaptive convolution kernels for cross-channel connections, which reduces the number of parameters of the attention mechanism without affecting its performance. In SSA, we design a sharpening filter suitable for spatial attention, which is used to enhance the attention to the edge position defects of railway tracks and enhance the detection effect of the network on edge defects. Second, we construct a microscale adaptive spatial feature fusion (M-ASFF), which adds a high-resolution feature extraction layer to enhance the details of the underlying features of tiny defects. At the same time, in order to prevent the loss of detailed information and the excessive increase of the parameters of the model, the low-resolution feature layer is removed. Combined with adaptive spatial feature fusion, it can prevent the semantic conflict caused by the fusion of features at different scales. Finally, given the lack of labeled public rail surface defect datasets, this paper is based on the collection of real rail images and manually labels defects to train an object detection network and open source it. The experimental results show that YOLOv5s-VF outperforms the existing rail surface defect detection methods with a detection accuracy of 93.5% and a detection speed of 114.9 fps.

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“…In [17], combined with the practical application scenarios of vehicle detection, the ratio of bounding box quantity and aspect ratio of the SSD algorithm is set to improve the speed of bounding box regression and improve the detection accuracy. In [18], on the basis of the traditional SSD algorithm, a full convolutional extrusion and excitation (FCSE) module is added, which can effectively detect various fabric defects. In [19], the context information awareness module is designed to promote the network's understanding of contextual information, and the detection accuracy of the improved algorithm reaches 77.8%, which is 0.6% higher than that of the SSD, and the detection effect is significantly improved in the image where it is difficult to distinguish between objects and backgrounds.…”

Section: Introductionmentioning

confidence: 99%

Urban Tram Track Obstacle Detection Algorithm based on BF-SSD

Wang¹,

Peng²,

Li³

et al. 2022

Preprint

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In order to improve the detection accuracy of urban tram track obstacles running in complex environments, a track obstacle detection algorithm based on BF-SSD is proposed. Design convolutional splitting structure, replace all 3*3 convolutional layers in VGG16 except the first convolutional layer, improve the detection speed, design two-way fusion modules, strengthen the feature expression ability of the low-level feature layer, improve the detection accuracy of small targets, design a two-stage deconvolution module, make up for the noise information generated by the high-level feature layer due to excessive deconvolution, enhance the detection effect in harsh environments, propose to improve NMS, and improve the detection ability of coincident targets. Through the test and analysis of the obstacle image dataset of the self-made normal environment and the harsh environment, the experimental results show that compared with other algorithms, the loss function of the BF-SSD algorithm is the lowest, and the mAP of the BF-SSD algorithm reaches 90.03% and 87.83% respectively under normal weather and bad weather environments, and the mAP of the detection of small targets reaches 70.26% and 69.26%, respectively. The BF-SSD algorithm improves the detection accuracy of tram obstacles, especially the detection ability of small target obstacles.

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An Improved Fabric Defect Detection Method Based on SSD

Cited by 14 publications

References 37 publications

Defect detection algorithm for fabric based on deformable convolutional network

Defect detection algorithm for fabric based on deformable convolutional network

Detection of Surface Defects on Railway Tracks Based on Deep Learning

Urban Tram Track Obstacle Detection Algorithm based on BF-SSD

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