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

Fabrics play a pivotal role in human life and production, and surface defects can directly affect the quality and value of fabrics. Many methods for fabric defect detection have been proposed, but tiny defects are still difficult to be detected effectively, and the accuracy of defect localization and classification is low. To address these issues, a modified YOLOX network called YOLOX-CATD is proposed, which was supplemented with a coordinate attention module (CAM) and tiny defect detection layer (TDDL) for fast and efficient detection of fabric defects, especially tiny defects. Firstly, the anchor-free network is used as the detection framework to avoid the influence of hyperparameters of the setting anchor. Secondly, a CAM is proposed to enhance the representation of the object of interest in the input feature map and suppress the background regions. Finally, a TDDL is added to introduce high-resolution features to improve the localization accuracy of tiny defects. The experimental results on the Aliyun Tianchi Fabric dataset and NEU-DET demonstrate the superiority and generalization of the modified model. The mean average precision (mAP) of YOLOX-CATD on the fabric defect dataset is improved by 5.67% compared to the original YOLOX, and the detection speed can reach 35-36 frames per second (FPS). This proves that YOLOX-CATD can obtain excellent fabric defect detection performance and meet the urgent need for real-time detection in industrial applications.

Section: Yoloxmentioning

confidence: 99%

Fabric defect detection based on anchor-free network

Wang

Fang

Xiang

2023

“…In recent years, with the tremendous success of deep learning in various fields, the industry has gradually applied deep learning methods, especially network models such as image classification and semantic segmentation, to surface defect detection [8][9][10][11][12]. In [10], Xie et al proposed an enhanced algorithm based on YOLOv5n to improve the accuracy and reliability of bearing roller end surface defect detection in industrial production. Zeng et al [11] introduced an enhanced semantic segmentation algorithm based on DeepLabv3+ for the automated detection of defects in aviation ferromagnetic components.…”

Section: Introductionmentioning

confidence: 99%

Automated measurement of smartphone screen scratches using visual methods

Zhao,

Yao,

et al. 2024

Quantitative measurement of smartphone screen scratches is crucial for pricing in the used smartphone market. Traditional manual visual inspection methods suffer from inherent limitations, namely being labor-intensive, subjective, and prone to inaccuracy. Hence, this study proposes a vision-based measurement method as a viable solution to overcome these challenges. The algorithm uses the Hessian enhancement to extract scratch features, applies adaptive thresholding to distinguish features from the background, and employs morphological reconstruction to reconstruct complete scratches. The topological analysis splits and mergers intersecting scratches, enabling individual segmentation. Finally, four metrics for measuring screen scratches include length, brightness, contrast, and maximum width to quantitatively characterize the damage of screen scratches. Experiments showed that the proposed algorithm outperforms other vision-based methods, with an accuracy of 99.6% in estimating the scratch length and a running time of 43.7 ms, which fully meets the efficiency and accuracy requirements of industrial application.

“…Recently, the majority of intelligent defect detection algorithms have been developed by enhancing the YOLO model [9][10][11][12][13]. Liu and Ma [14] incorporated a dilated weighted across a stages-feature pyramid network into their model to adaptively adjust the receptive field and attention weight preference of the output feature maps at various scales.…”

Section: Introductionmentioning

confidence: 99%

Adaptive receptive field based on multi-size convolution kernel for micro-defect detection of turbine blades

Liu,

Yuan,

Ren

et al. 2023

Because turbine blades are essential parts of aircraft engines, micro-defects on the surface of turbine blades induce accidents in aircraft crashes. The detection of micro-defects in aerospace turbine blades is achieved within the realm of non-destructive evaluation. Given that most of the defects are tiny and spread randomly on a curved surface, which cannot be completely detected by current target detection methods, it is feasible for micro-defects to be effectively detected by the fluorescent magnetic particle method. In this paper, we propose a Defect Classification (DCLS)-Deformable Detection Transformer (DETR) model to inspect micro-defects in an automatic fluorescent magnetic particle inspection system. In the DCLS-Deformable DETR model, an adaptive receptive field (ARF) attention module is designed, which considers the interdependencies between the channel features of different feature maps. The weights between the channels of each feature map were also considered, while adaptively adjusting the receptive field according to the detection target size. The DCLS-Deformable DETR model with ARF increased the AP from 63.4% to 64% and AP50 from 95.2% to 97.2%, compared to the Deformable DETR. Turbine blades include three typical defects: linear cracks, reticular cracks, and pitting corrosion. Compared with the original model, the proposed model enhances the AP of three defect types by 1.8%, 2% and 4.7% respectively. The DCLS-Deformable DETR model considers the position, level information, and channel information of the input samples, which can capture micro-defects around large defects.