Fabric Defect Detection Algorithm Based on Image Saliency Region and Similarity Location

Li, Weiwei; Zhang, Zijing; Wang, Mingyue; Chen, Hang

doi:10.3390/electronics12061392

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…Figures 4 and 5 show an outline of the procedure for generating the saliency map proposed in this study [21]. In essence, images consist of the colors R, G, and B, each with its distinct frequency values.…”

Section: Generation Of Saliency Mapmentioning

confidence: 99%

Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

Lee,

Kim,

Park

et al. 2023

Energies

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This study proposes a model for transient stability assessment, which is a convolutional neural network model combined with a saliency map (S–CNN model). The convolutional neural network model is trained on dynamic data acquired through the data measurement devices of a power system. Applying the saliency map to the acquired dynamic data visually highlights the critical aspects of transient stability assessment. This reduces data training time by eliminating unnecessary aspects during the convolutional neural network model training, thus improving training efficiency. As a result, the proposed model can achieve high performance in transient stability assessment. The dynamic data are acquired by configuring benchmark models, IEEE 39 and 118 bus systems, through MATLAB/Simulink and performing time-domain simulations. Based on the acquired dynamic data, the performance of the proposed model is verified through a confusion matrix. Furthermore, an analysis of the effects of noise interference on the performance is conducted.

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Section: Generation Of Saliency Mapmentioning

confidence: 99%

Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

Lee,

Kim,

Park

et al. 2023

Energies

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“…First, the IQR (interquartile range) statistical method is used to analyze outliers, so that outliers = value < (Q1−1.5IQR) or value > (Q3+1.5IQR), as well as Li et al (2023). Of the 13232 readings, 287 data points were removed (2.16% outliers) and the remaining data are presented in Figure 8.…”

Section: Reference Sensor (Sensor 13) X Temperaturementioning

confidence: 99%

Low-cost ultrasonic sensors for in-field experimentation data collection

Theodoro

Sanches

Cruz³

et al. 2023

Ciênc. agrotec.

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Surface runoff monitoring is important for the sustainable management of global water resources. Obtaining a practical and inexpensive method for collecting data in the field can help to better understand surface runoff and its effects, necessary for the management of watersheds. This study sought to elaborate the calibration curves of the ultrasonic sensor due to temperature variability, verifying the inaccuracy of the distance between objects and the sensor, and determining the feasibility of using low-cost sensors in an in-loco experiment installed on Parshall flumes. The experiment was conducted on the Experimental Farm of the Federal University of Grande Dourados, Dourados, MS, Brazil. The data were collected by twelve HC-SR04 ultrasonic distance sensors , which were coupled to a data acquisition system composed of an expansion board connected to a Raspberry minicomputer. Sensor calibration using temperature data resulted in the error correction of ± 8.0 mm of distance reading. On the other hand, the R2 of the comparison curves between sensor and control system (laser distance meter and ruler in the flume) resulted in high values (above 0.95), showing the feasibility of its use and meeting the specifications for use in the field subject to weather conditions. This study demonstrates the performance of ultrasonic sensors as a potential for new application to evaluate surface runoff aiming to propose new runoff coefficients.

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“…The traditional methods include the texture structure method, histogram statistics, spectral method, modeling method and adaptive dictionary learning method for fabric defect detection. Li et al put forward an approach that relies on saliency region and similarity localization detection to achieve accurate contour segmentation of periodic textured fabric images and address the challenge of defect detection [2]. This detection speed is not dominant, and the detection effect for fabric images with no obvious defects or complex texture fabric images is generally not satisfactory.…”

Section: Introductionmentioning

confidence: 99%

PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection

Liu,

Wang,

Cao

et al. 2024

Applied Sciences

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Defect detection holds significant importance in improving the overall quality of fabric manufacturing. To improve the effectiveness and accuracy of fabric defect detection, we propose the PRC-Light YOLO model for fabric defect detection and establish a detection system. Firstly, we have improved YOLOv7 by integrating new convolution operators into the Extended-Efficient Layer Aggregation Network for optimized feature extraction, reducing computations while capturing spatial features effectively. Secondly, to enhance the performance of the feature fusion network, we use Receptive Field Block as the feature pyramid of YOLOv7 and introduce Content-Aware ReAssembly of FEatures as upsampling operators for PRC-Light YOLO. By generating real-time adaptive convolution kernels, this module extends the receptive field, thereby gathering vital information from contexts with richer content. To further optimize the efficiency of model training, we apply the HardSwish activation function. Additionally, the bounding box loss function adopts the Wise-IOU v3, which incorporates a dynamic non-monotonic focusing mechanism that mitigates adverse gradients from low-quality instances. Finally, in order to enhance the PRC-Light YOLO model’s generalization ability, we apply data augmentation techniques to the fabric dataset. In comparison to the YOLOv7 model, multiple experiments indicate that our proposed fabric defect detection model exhibits a decrease of 18.03% in model parameters and 20.53% in computational load. At the same time, it has a notable 7.6% improvement in mAP.

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Fabric Defect Detection Algorithm Based on Image Saliency Region and Similarity Location

Cited by 8 publications

References 14 publications

Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

Low-cost ultrasonic sensors for in-field experimentation data collection

PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection

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